{
  "capped": false,
  "live": 374,
  "meta": 383,
  "kernels": [
    {
      "ref": "segamega/nfl-big-data-bowl-official-starter-notebook",
      "title": "NFL Big Data Bowl Official Starter Notebook",
      "source": "live",
      "kernel_id": "135516582"
    },
    {
      "ref": "nehakotha/predicting-receiver-routes",
      "title": "Predicting Receiver Routes",
      "source": "live",
      "kernel_id": "74132306"
    },
    {
      "ref": "annettelianchiu/nfl-predictions-rc",
      "title": "NFL_Predictions_RC",
      "source": "live",
      "kernel_id": "72474988"
    },
    {
      "ref": "alexanderkireev/04-ml-trainings-nlp",
      "title": "04_ml_trainings_nlp",
      "source": "live",
      "kernel_id": "70487269"
    },
    {
      "ref": "rishabhpathak/nfl-2020-big-data-bowl",
      "title": "NFL 2020 Big data bowl",
      "source": "live",
      "kernel_id": "65338162"
    },
    {
      "ref": "thesnak/nfl-prediction-with-pyspark",
      "title": "NFL Prediction with PySpark",
      "source": "live",
      "kernel_id": "65124729"
    },
    {
      "ref": "chuanhnguyn/jamie-plotting-player-position",
      "title": "Jamie - Plotting Player Position",
      "source": "live",
      "kernel_id": "53952837"
    },
    {
      "ref": "wannapatice/039-icewannapat",
      "title": "039_Icewannapat",
      "source": "live",
      "kernel_id": "53786145"
    },
    {
      "ref": "ratchanonkocharat/036-ratchanon",
      "title": "036 Ratchanon",
      "source": "live",
      "kernel_id": "53786063"
    },
    {
      "ref": "thanawatmayures/nfl-big-data-bowl-028thanawat",
      "title": "NFL Big Data Bowl - 028Thanawat",
      "source": "live",
      "kernel_id": "53782759"
    },
    {
      "ref": "nackii/nfl-browl-2021-007chakraphat",
      "title": "NFL Browl 2021-007Chakraphat",
      "source": "live",
      "kernel_id": "53782277"
    },
    {
      "ref": "abowen/open-field-tackling-insights-for-nfl-coaches",
      "title": "Open Field Tackling Insights for NFL Coaches",
      "source": "live",
      "kernel_id": "49123059"
    },
    {
      "ref": "codyjanderson/nfl-predictions",
      "title": "NFL_Predictions",
      "source": "live",
      "kernel_id": "47608251"
    },
    {
      "ref": "colepeterman/predicting-nfl-offensive-formations",
      "title": "Predicting NFL Offensive Formations",
      "source": "live",
      "kernel_id": "34980782"
    },
    {
      "ref": "ahmettalhabektas/how-we-c-n-deal-w-th-mis-ing-data",
      "title": "How we c☐n deal w☐th mis☐ing data?",
      "source": "live",
      "kernel_id": "34039351"
    },
    {
      "ref": "athirah2012/quaterback-nfl-2023",
      "title": "Quaterback_NFL_2023",
      "source": "live",
      "kernel_id": "33953819"
    },
    {
      "ref": "s903124/bdb-model-10-feat-flax",
      "title": "bdb model 10 feat Flax",
      "source": "live",
      "kernel_id": "28385430"
    },
    {
      "ref": "reef3152/nfl-big-data-bowl-notebook",
      "title": "nfl_big_data_bowl_notebook",
      "source": "live",
      "kernel_id": "22254730"
    },
    {
      "ref": "righthandflag/nfl-simple-model-using-lightgbm",
      "title": "NFL simple model using lightGBM",
      "source": "live",
      "kernel_id": "17906088"
    },
    {
      "ref": "linsistx/nfl-simplified-v1",
      "title": "NFL_Simplified_v1",
      "source": "live",
      "kernel_id": "17886799"
    },
    {
      "ref": "linsistx/data-science-fa19-nfl-intro",
      "title": "data-science-fa19-nfl-intro",
      "source": "live",
      "kernel_id": "17663455"
    },
    {
      "ref": "linsistx/nfl-simple-model-using-lightgbm-from-scratch",
      "title": "NFL simple model using lightGBM(from scratch)",
      "source": "live",
      "kernel_id": "17662497"
    },
    {
      "ref": "liueee/1nfl-simplified-v1-train",
      "title": "1NFL_Simplified_v1_train",
      "source": "live",
      "kernel_id": "17549197"
    },
    {
      "ref": "pburke1977/nfl-big-data-bowl-plotting-player-position",
      "title": "NFL Big Data Bowl - Plotting Player Position",
      "source": "live",
      "kernel_id": "17167721"
    },
    {
      "ref": "pirogovskiy/course-project-nfl",
      "title": "Course project (NFL)",
      "source": "live",
      "kernel_id": "16918553"
    },
    {
      "ref": "ionlights/projects-fa19-nfl-deep-dive",
      "title": "projects-fa19-nfl-deep-dive",
      "source": "live",
      "kernel_id": "14308650"
    },
    {
      "ref": "ionlights/projects-fa19-nfl-intro",
      "title": "projects-fa19-nfl-intro",
      "source": "live",
      "kernel_id": "14308645"
    },
    {
      "ref": "s903124/bdb-model-10-feat-pytorch",
      "title": "bdb model 10 feat Pytorch",
      "source": "live",
      "kernel_id": "14221879"
    },
    {
      "ref": "srlee6/fork-of-neural-networks-different-2-comm",
      "title": "fork of neural networks + different 2 comm",
      "source": "live",
      "kernel_id": "13741942"
    },
    {
      "ref": "gabrielpinheiropires/nfl-rushing-yards",
      "title": "NFL Rushing Yards",
      "source": "live",
      "kernel_id": "11789648"
    },
    {
      "ref": "elvinagammed/simple-automl-eda",
      "title": "Simple AutoML EDA",
      "source": "live",
      "kernel_id": "11639603"
    },
    {
      "ref": "manuelreta/neural-networks-feature-engineering-for-the-win",
      "title": "neural networks + feature engineering for the win",
      "source": "live",
      "kernel_id": "10009517"
    },
    {
      "ref": "s903124/fork-of-pytorch-transformer-public-14th-private",
      "title": "Fork of Pytorch Transformer (public 14th / private",
      "source": "live",
      "kernel_id": "9643829"
    },
    {
      "ref": "s903124/visualize-nfl-run-play-with-captum",
      "title": "Visualize NFL run play with captum",
      "source": "live",
      "kernel_id": "9448385"
    },
    {
      "ref": "arrowheadanalytics/ethan-model",
      "title": "Ethan_Model",
      "source": "live",
      "kernel_id": "9269911"
    },
    {
      "ref": "s903124/fast-pitch-control",
      "title": "Fast Pitch Control",
      "source": "live",
      "kernel_id": "8946027"
    },
    {
      "ref": "itsmijo/kernel59b4d9463f",
      "title": "kernel59b4d9463f",
      "source": "live",
      "kernel_id": "8916752"
    },
    {
      "ref": "ekethio/nfl-rb-analysis",
      "title": "NFL RB analysis",
      "source": "live",
      "kernel_id": "8777536"
    },
    {
      "ref": "seanyman84/nfl-rush-prediction",
      "title": "NFL Rush Prediction",
      "source": "live",
      "kernel_id": "8343717"
    },
    {
      "ref": "eitharalfatih/eda-and-model",
      "title": "EDA and Model",
      "source": "live",
      "kernel_id": "8067871"
    },
    {
      "ref": "mojopriest/running-back-vs-linebacker-a-practice-notebook",
      "title": "Running Back vs. Linebacker: A Practice Notebook",
      "source": "live",
      "kernel_id": "7863246"
    },
    {
      "ref": "holoong9291/eda-for-nfl-have-fun",
      "title": "EDA for NFL, have fun",
      "source": "live",
      "kernel_id": "7793640"
    },
    {
      "ref": "s903124/pytorch-fixed-random-synthesizer",
      "title": "Pytorch fixed random synthesizer",
      "source": "live",
      "kernel_id": "7765387"
    },
    {
      "ref": "nyanpn/pytorch-transformer-public-14th-private-22nd",
      "title": "Pytorch Transformer (public 14th / private 22nd)",
      "source": "live",
      "kernel_id": "7756862"
    },
    {
      "ref": "ttya16/nfl-challenge-notebook-latesub",
      "title": "nfl_challenge_notebook_latesub",
      "source": "live",
      "kernel_id": "7723814"
    },
    {
      "ref": "vaibhavsxn/the-talented",
      "title": "The Talented",
      "source": "live",
      "kernel_id": "7633380"
    },
    {
      "ref": "robertehshi/basics-of-nfl-expost",
      "title": "Basics_of_NFL_expost",
      "source": "live",
      "kernel_id": "7475114"
    },
    {
      "ref": "jpmiller/nfl-1standfuture-report",
      "title": "NFL 1standFuture Report 🏈",
      "source": "live",
      "kernel_id": "7301726"
    },
    {
      "ref": "nitindig/neural-networks-feature-engineering-for-the-win",
      "title": "neural networks + feature engineering for the win",
      "source": "live",
      "kernel_id": "7264341"
    },
    {
      "ref": "rishabhds/eda-nfl",
      "title": "EDA NFL",
      "source": "live",
      "kernel_id": "7068432"
    },
    {
      "ref": "sasrdw/zoo-keras",
      "title": "zoo_keras",
      "source": "live",
      "kernel_id": "7030730"
    },
    {
      "ref": "takuok/1st-place-reproduction-10feats-dev",
      "title": "1st place reproduction 10feats dev",
      "source": "live",
      "kernel_id": "7027601"
    },
    {
      "ref": "gertjac/final-leaderboard-predictions",
      "title": "final leaderboard predictions",
      "source": "live",
      "kernel_id": "7006797"
    },
    {
      "ref": "toshinoue/historical-public-lb-of-nfl-big-data-bowl",
      "title": "Historical Public LB of NFL Big Data bowl",
      "source": "live",
      "kernel_id": "6993608"
    },
    {
      "ref": "jtrotman/save-nfl-leaderboard",
      "title": "Save NFL Leaderboard",
      "source": "live",
      "kernel_id": "6963773"
    },
    {
      "ref": "sryo188558/how-to-evaluate-nfl-player-s-contribution",
      "title": "How to evaluate NFL player's contribution ?",
      "source": "live",
      "kernel_id": "6945940"
    },
    {
      "ref": "charlessuhartanto/graph-transfomer-from-cpmp-s-kernel",
      "title": "Graph Transfomer_from CPMP's kernel",
      "source": "live",
      "kernel_id": "6928106"
    },
    {
      "ref": "jackruggiero/submission",
      "title": "Submission",
      "source": "live",
      "kernel_id": "6856516"
    },
    {
      "ref": "vbmokin/nfl-feature-importance-xgb-lgbm-linreg",
      "title": "NFL feature importance - xgb, lgbm, linreg",
      "source": "live",
      "kernel_id": "6837120"
    },
    {
      "ref": "yawata/voronoi-area-in-front-of-the-rusher",
      "title": "Voronoi area in front of the rusher",
      "source": "live",
      "kernel_id": "6816267"
    },
    {
      "ref": "authman/lgbbbbbb",
      "title": "LGBBBBBB",
      "source": "live",
      "kernel_id": "6799409"
    },
    {
      "ref": "authman/nofe-final",
      "title": "NoFE FINAL",
      "source": "live",
      "kernel_id": "6799333"
    },
    {
      "ref": "robikscube/0-01348-stage-1-exploding-lightgbm-solution",
      "title": "[0.01348 Stage 1] - Exploding LightGBM Solution ",
      "source": "live",
      "kernel_id": "6798810"
    },
    {
      "ref": "somnambwl/nfl-2019-nn",
      "title": "NFL 2019 - NN ",
      "source": "live",
      "kernel_id": "6796971"
    },
    {
      "ref": "artemshramko/voronoi-pc-def-pos-nn",
      "title": "Voronoi + PC+Def+Pos | NN",
      "source": "live",
      "kernel_id": "6794598"
    },
    {
      "ref": "cadeknox/model-and-explanation",
      "title": "Model and Explanation",
      "source": "live",
      "kernel_id": "6794515"
    },
    {
      "ref": "samtaylor54321/nfl-big-data-bowl-2019",
      "title": "NFL Big Data Bowl 2019",
      "source": "live",
      "kernel_id": "6793079"
    },
    {
      "ref": "cpmpml/graph-transfomer",
      "title": "Graph Transfomer",
      "source": "live",
      "kernel_id": "6784355"
    },
    {
      "ref": "corbbb1111/nfl-yards-per-carry-eda",
      "title": "NFL Yards Per Carry EDA",
      "source": "live",
      "kernel_id": "6783649"
    },
    {
      "ref": "pashadude/catboost",
      "title": "catboost",
      "source": "live",
      "kernel_id": "6783122"
    },
    {
      "ref": "matheuspush/rushin-to-the-top",
      "title": "Rushin' to the top!",
      "source": "live",
      "kernel_id": "6779783"
    },
    {
      "ref": "wimwim/nfl-transformer-final-run",
      "title": "NFL-transformer-final-run",
      "source": "live",
      "kernel_id": "6778727"
    },
    {
      "ref": "nisheethjaiswal/nfl-eda-nn-analysis",
      "title": "NFL - EDA & NN Analysis",
      "source": "live",
      "kernel_id": "6777263"
    },
    {
      "ref": "aksha17/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6775253"
    },
    {
      "ref": "lazyer/nfl-pipeline-v4",
      "title": "NFL_pipeline_v4",
      "source": "live",
      "kernel_id": "6773271"
    },
    {
      "ref": "sharmistha96/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6772777"
    },
    {
      "ref": "jonbown/improved-keras-nn",
      "title": "Improved Keras NN",
      "source": "live",
      "kernel_id": "6770929"
    },
    {
      "ref": "mcurry1/neuralnet",
      "title": "NeuralNet",
      "source": "live",
      "kernel_id": "6770601"
    },
    {
      "ref": "sherkt1/final-hole-metric-features-v5-0",
      "title": "Final Hole Metric Features, v5.0",
      "source": "live",
      "kernel_id": "6770413"
    },
    {
      "ref": "artemshramko/voronoi-play-control-fe",
      "title": "Voronoi + Play Control + FE",
      "source": "live",
      "kernel_id": "6768135"
    },
    {
      "ref": "sylvainmichel/nfl202-swish",
      "title": "nfl202 swish",
      "source": "live",
      "kernel_id": "6765410"
    },
    {
      "ref": "krammerg/nfl-baseline-nn-v4",
      "title": "NFL Baseline NN v4",
      "source": "live",
      "kernel_id": "6757747"
    },
    {
      "ref": "funaki/cat-boost-of-funaki-optuna",
      "title": "Cat boost of funaki Optuna",
      "source": "live",
      "kernel_id": "6756005"
    },
    {
      "ref": "jccampos/nfl-2020-winner-solution-the-zoo",
      "title": "NFL-2020-Winner-Solution(The Zoo)",
      "source": "live",
      "kernel_id": "6755191"
    },
    {
      "ref": "jinangefrank/nfl-lgb-adjust-params-based-on-xingyuan",
      "title": "NFL - LGB - adjust params based on xingyuan - 这个能跑",
      "source": "live",
      "kernel_id": "6751470"
    },
    {
      "ref": "jagannathrk/nfl-big-data-bowl-model-using-tf-lstm",
      "title": "NFL Big Data Bowl model using TF LSTM ",
      "source": "live",
      "kernel_id": "6747265"
    },
    {
      "ref": "s13658695/simple-xgb-simple-fe",
      "title": "Simple XGB + Simple FE",
      "source": "live",
      "kernel_id": "6746986"
    },
    {
      "ref": "jagannathrk/nfl-big-data-bowl-model-using-lightgbm",
      "title": "NFL Big Data Bowl model using LightGBM",
      "source": "live",
      "kernel_id": "6746945"
    },
    {
      "ref": "oliveia/nfl-big-data-bowl-with-nn-keras",
      "title": "NFL Big Data Bowl with NN [keras]",
      "source": "live",
      "kernel_id": "6741182"
    },
    {
      "ref": "ivakat/mainn",
      "title": "Mainn",
      "source": "live",
      "kernel_id": "6739102"
    },
    {
      "ref": "oliveia/nfl-big-data-bowl-with-random-forests-k-fold",
      "title": "NFL Big Data Bowl with Random Forests k-Fold",
      "source": "live",
      "kernel_id": "6735095"
    },
    {
      "ref": "enzoamp/location-eda-with-combined-rusher-features",
      "title": "Location EDA with combined rusher features",
      "source": "live",
      "kernel_id": "6733107"
    },
    {
      "ref": "aleksthegreat/lstm-final-working",
      "title": "LSTM Final Working",
      "source": "live",
      "kernel_id": "6729895"
    },
    {
      "ref": "yuminson/neural-network-with-mae-objective-0-01385",
      "title": "neural network, with mae objective, 0.01385",
      "source": "live",
      "kernel_id": "6718852"
    },
    {
      "ref": "yukomiya/komiya-nfl-catboost",
      "title": "Komiya_NFL_CatBoost",
      "source": "live",
      "kernel_id": "6706730"
    },
    {
      "ref": "shozendan/davis-ds-club-a-neural-network-approach",
      "title": "Davis DS Club: a Neural Network Approach",
      "source": "live",
      "kernel_id": "6704384"
    },
    {
      "ref": "funaki/fork-of-random-forest-classifier-of-funaki-optuna",
      "title": "Fork of Random_forest_classifier of funaki Optuna",
      "source": "live",
      "kernel_id": "6702746"
    },
    {
      "ref": "pwendel3/generational-talent",
      "title": "generational talent",
      "source": "live",
      "kernel_id": "6687439"
    },
    {
      "ref": "stefancurtress/nfl-simplified-v1",
      "title": "NFL_Simplified_v1",
      "source": "live",
      "kernel_id": "6686981"
    },
    {
      "ref": "chichengzhang/gary-nfl-big-data-bowl",
      "title": "Gary - NFL Big Data Bowl",
      "source": "live",
      "kernel_id": "6686659"
    },
    {
      "ref": "adityakumarsinha/firstkernel",
      "title": "FirstKernel",
      "source": "live",
      "kernel_id": "6686186"
    },
    {
      "ref": "dandrocec/location-eda-with-rusher-features",
      "title": "Location EDA with rusher features",
      "source": "live",
      "kernel_id": "6679380"
    },
    {
      "ref": "aimlanalytics/kernel1bcd254968",
      "title": "kernel1bcd254968",
      "source": "live",
      "kernel_id": "6675262"
    },
    {
      "ref": "gogo827jz/bayesian-hyperparameter-tuning-nn-rf",
      "title": "Bayesian Hyperparameter Tuning (NN&RF)",
      "source": "live",
      "kernel_id": "6674441"
    },
    {
      "ref": "gdpsgdps/location-eda-8eb410",
      "title": "Location EDA 8eb410",
      "source": "live",
      "kernel_id": "6671177"
    },
    {
      "ref": "yutafm/nfl-ef",
      "title": "NFL EF",
      "source": "live",
      "kernel_id": "6670816"
    },
    {
      "ref": "zachanhalt/testtry",
      "title": "TestTry",
      "source": "live",
      "kernel_id": "6670513"
    },
    {
      "ref": "dmintry/stack-rf-cb",
      "title": "Stack rf, cb",
      "source": "live",
      "kernel_id": "6669370"
    },
    {
      "ref": "oliveia/nfl-big-data-bowl-with-random-forests",
      "title": "NFL Big Data Bowl with Random Forests",
      "source": "live",
      "kernel_id": "6666028"
    },
    {
      "ref": "oliveia/nfl-big-data-bowl-with-knn",
      "title": "NFL Big Data Bowl with kNN",
      "source": "live",
      "kernel_id": "6657777"
    },
    {
      "ref": "gdpsgdps/nfl-001-neural-network",
      "title": "[NFL] - [001]- [Neural Network]",
      "source": "live",
      "kernel_id": "6657080"
    },
    {
      "ref": "thecansin/eda-nfl-big-data-bowl",
      "title": "EDA NFL Big Data Bowl ",
      "source": "live",
      "kernel_id": "6656187"
    },
    {
      "ref": "alexgeiger/the-official-nfl-solution",
      "title": "The Official NFL Solution",
      "source": "live",
      "kernel_id": "6654697"
    },
    {
      "ref": "holoong9291/nfl-preprocessing-missing-value-and-outlier",
      "title": "NFL Preprocessing : missing value and outlier",
      "source": "live",
      "kernel_id": "6654308"
    },
    {
      "ref": "mtodisco10/2019-big-data-bowl-submission",
      "title": "2019 Big Data Bowl Submission",
      "source": "live",
      "kernel_id": "6649355"
    },
    {
      "ref": "dmintry/catboost",
      "title": "catboost",
      "source": "live",
      "kernel_id": "6644832"
    },
    {
      "ref": "holoong9291/nfl-tracking-wrangling-voronoi-and-sonars-python",
      "title": "NFL tracking: wrangling,Voronoi,and sonars-Python",
      "source": "live",
      "kernel_id": "6643245"
    },
    {
      "ref": "gogo827jz/blending-nn-and-lgbm-rf",
      "title": "Blending NN and LGBM/RF",
      "source": "live",
      "kernel_id": "6640962"
    },
    {
      "ref": "scirpus/location-eda-8eb410-556827",
      "title": "Location EDA 8eb410 556827",
      "source": "live",
      "kernel_id": "6637331"
    },
    {
      "ref": "scirpus/kullbach-leibler-genetic-programming-gubbins",
      "title": "Kullbach Leibler Genetic Programming Gubbins",
      "source": "live",
      "kernel_id": "6635814"
    },
    {
      "ref": "anandavati/ngboost-for-nfl",
      "title": "NGBoost for NFL",
      "source": "live",
      "kernel_id": "6634270"
    },
    {
      "ref": "nickyg/nfl-pytorch-fastai-one-cycle-policy-training",
      "title": "NFL Pytorch/fastai --One cycle policy training",
      "source": "live",
      "kernel_id": "6629456"
    },
    {
      "ref": "s13658695/xgboost-earlystopping-no-cv",
      "title": "XGBoost_EarlyStopping (NO CV) ",
      "source": "live",
      "kernel_id": "6626048"
    },
    {
      "ref": "holoong9291/nfl-big-data-bowl",
      "title": "NFL Big Data Bowl",
      "source": "live",
      "kernel_id": "6620470"
    },
    {
      "ref": "danoff/neural-networks-isaih-divya-charlie-version",
      "title": "neural networks - isaih divya charlie version",
      "source": "live",
      "kernel_id": "6616900"
    },
    {
      "ref": "funaki/random-forest-classifier-of-funaki-optuna",
      "title": "Random_forest_classifier of funaki Optuna",
      "source": "live",
      "kernel_id": "6616779"
    },
    {
      "ref": "jasonrockphelps/best-etc-on-cdf-model",
      "title": "Best ETC on CDF Model",
      "source": "live",
      "kernel_id": "6610566"
    },
    {
      "ref": "christoffer/random-walk-feature-selection",
      "title": "Random Walk Feature Selection",
      "source": "live",
      "kernel_id": "6609163"
    },
    {
      "ref": "blazejd/nfl-fixed-bugs",
      "title": "NFL fixed bugs",
      "source": "live",
      "kernel_id": "6607214"
    },
    {
      "ref": "gertjac/regression-approach",
      "title": "Regression approach",
      "source": "live",
      "kernel_id": "6598740"
    },
    {
      "ref": "jpmary/lgbm-f",
      "title": "lgbm F",
      "source": "live",
      "kernel_id": "6598318"
    },
    {
      "ref": "s13658695/nfl-xgboost-simple",
      "title": "NFL_XGBoost_Simple",
      "source": "live",
      "kernel_id": "6593158"
    },
    {
      "ref": "funaki/cat-boost-regressor-of-funaki",
      "title": "Cat_Boost_Regressor of funaki",
      "source": "live",
      "kernel_id": "6590089"
    },
    {
      "ref": "kaushal2896/nfl-big-data-bowl-fe-catboost",
      "title": "NFL Big Data Bowl: FE + CatBoost",
      "source": "live",
      "kernel_id": "6586200"
    },
    {
      "ref": "charlessuhartanto/nfl-voronoi",
      "title": "NFL_Voronoi",
      "source": "live",
      "kernel_id": "6585887"
    },
    {
      "ref": "arihantkumarjain/nfl-fe",
      "title": "NFL-FE",
      "source": "live",
      "kernel_id": "6571949"
    },
    {
      "ref": "hrsh31/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570668"
    },
    {
      "ref": "arun051298/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570655"
    },
    {
      "ref": "frkfaiz/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570478"
    },
    {
      "ref": "apoorvaappz/next-gen-eda-sample",
      "title": "Next Gen EDA Sample",
      "source": "live",
      "kernel_id": "6570442"
    },
    {
      "ref": "amulyamanne/next-gen-eda-sample",
      "title": "Next Gen EDA-Sample",
      "source": "live",
      "kernel_id": "6570435"
    },
    {
      "ref": "sakshi7896/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570427"
    },
    {
      "ref": "pavitraaritas/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570411"
    },
    {
      "ref": "sahanabalappa/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570402"
    },
    {
      "ref": "infinitevictory/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570393"
    },
    {
      "ref": "vensadk/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570384"
    },
    {
      "ref": "kamalbannuru/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570382"
    },
    {
      "ref": "rakeshmariyaplar/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570381"
    },
    {
      "ref": "pratikgarg02/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570377"
    },
    {
      "ref": "nikhilbhat06/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570372"
    },
    {
      "ref": "enamkaggle/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570367"
    },
    {
      "ref": "adithyabalakumar/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570366"
    },
    {
      "ref": "sameerkashya/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570364"
    },
    {
      "ref": "dilipchaudhary/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570362"
    },
    {
      "ref": "hemanthkumarr/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570361"
    },
    {
      "ref": "juhi1994/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570360"
    },
    {
      "ref": "ashish1732/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570357"
    },
    {
      "ref": "nuthisriram/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570355"
    },
    {
      "ref": "preethamms/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6570348"
    },
    {
      "ref": "yathishnv/next-gen-eda-66b064",
      "title": "Next Gen EDA 66b064",
      "source": "live",
      "kernel_id": "6570332"
    },
    {
      "ref": "go5kuramubon/nfl-mixturedensitynetworks-output-gmm-param-nn",
      "title": "[NFL]MixtureDensityNetworks(output GMM param NN)",
      "source": "live",
      "kernel_id": "6570302"
    },
    {
      "ref": "davidcairuz/i-hope-eda-comes-with-practice",
      "title": "I hope EDA comes with practice",
      "source": "live",
      "kernel_id": "6568262"
    },
    {
      "ref": "abugim/nfl-gameplay-visualization",
      "title": "NFL GamePlay Visualization",
      "source": "live",
      "kernel_id": "6564592"
    },
    {
      "ref": "ekhtiar/features-features-features",
      "title": "Features, Features, Features",
      "source": "live",
      "kernel_id": "6562896"
    },
    {
      "ref": "rajesh372/kernel-final",
      "title": "kernel_final",
      "source": "live",
      "kernel_id": "6559821"
    },
    {
      "ref": "jonbown/simple-keras-nn",
      "title": "Simple Keras NN",
      "source": "live",
      "kernel_id": "6559151"
    },
    {
      "ref": "williamsne2/nfl-big-data-nw-final",
      "title": "NFL Big Data NW - Final",
      "source": "live",
      "kernel_id": "6557025"
    },
    {
      "ref": "peacemaket/nfl-data-bowl",
      "title": "NFL Data bowl",
      "source": "live",
      "kernel_id": "6552923"
    },
    {
      "ref": "kidrulit/nfl-big-data-feature-engineering-and-encoding",
      "title": "NFL Big Data - Feature Engineering and Encoding  ",
      "source": "live",
      "kernel_id": "6549159"
    },
    {
      "ref": "shishu1421/nfl-bowl-with-location",
      "title": "NFL Bowl with Location",
      "source": "live",
      "kernel_id": "6539862"
    },
    {
      "ref": "yukomiya/komiya-nfl-lgbm",
      "title": "Komiya_NFL_lgbm",
      "source": "live",
      "kernel_id": "6537552"
    },
    {
      "ref": "harshitholmes/nfl-trial6",
      "title": "NFL-Trial6",
      "source": "live",
      "kernel_id": "6528225"
    },
    {
      "ref": "gerwynng/nfl-bdb-starter-fe",
      "title": "[NFL-BDB] Starter FE",
      "source": "live",
      "kernel_id": "6528145"
    },
    {
      "ref": "joocheol/fe-lecture-20191109",
      "title": "FE_Lecture_20191109",
      "source": "live",
      "kernel_id": "6526303"
    },
    {
      "ref": "yukomiya/komiya-nfl-class3-submit",
      "title": "Komiya_NFL_class3_submit",
      "source": "live",
      "kernel_id": "6525481"
    },
    {
      "ref": "funaki/random-forest-classifier-of-funaki-nfl-3-tuning",
      "title": "Random_Forest_Classifier of funaki_NFL_3 tuning",
      "source": "live",
      "kernel_id": "6523728"
    },
    {
      "ref": "sanshengshi/lightgbm-clean-stadiumtype",
      "title": "LightGBM+clean StadiumType",
      "source": "live",
      "kernel_id": "6513499"
    },
    {
      "ref": "funaki/random-forest-classifier-of-funaki-nfl-3",
      "title": "Random_Forest_Classifier of funaki_NFL_3",
      "source": "live",
      "kernel_id": "6509485"
    },
    {
      "ref": "zachanhalt/work-kernel",
      "title": "Work Kernel",
      "source": "live",
      "kernel_id": "6508163"
    },
    {
      "ref": "tombresee/next-gen-eda",
      "title": "Next Gen EDA",
      "source": "live",
      "kernel_id": "6507838"
    },
    {
      "ref": "jasonrockphelps/tracking-preprocessing-py",
      "title": "tracking_preprocessing.py",
      "source": "live",
      "kernel_id": "6507526"
    },
    {
      "ref": "vbmokin/lgbm-multiple-classifier-with-max-depth-5",
      "title": "lgbm, multiple classifier with max_depth=5",
      "source": "live",
      "kernel_id": "6492203"
    },
    {
      "ref": "adibpriatama/nn-19-features",
      "title": "NN-19 features",
      "source": "live",
      "kernel_id": "6490687"
    },
    {
      "ref": "coolcoder22/nn-19-features",
      "title": "NN-19 features",
      "source": "live",
      "kernel_id": "6488735"
    },
    {
      "ref": "coolcoder22/nfl-001-feature-selection",
      "title": "[NFL] - [001]- [Feature selection]",
      "source": "live",
      "kernel_id": "6488640"
    },
    {
      "ref": "enzoamp/nfl-lightgbm",
      "title": "[NFL] - [LightGBM]",
      "source": "live",
      "kernel_id": "6487349"
    },
    {
      "ref": "kmtopel/kernel3555cf0128",
      "title": "kernel3555cf0128",
      "source": "live",
      "kernel_id": "6482425"
    },
    {
      "ref": "antoniorivero/nfl-v2",
      "title": "NFL v2",
      "source": "live",
      "kernel_id": "6481933"
    },
    {
      "ref": "cpmpml/save-test",
      "title": "save test",
      "source": "live",
      "kernel_id": "6480975"
    },
    {
      "ref": "abhikjha/cox-proportional-hazard-model",
      "title": "Cox proportional hazard model",
      "source": "live",
      "kernel_id": "6480968"
    },
    {
      "ref": "vbmokin/lgbm-multiple-classifier-with-max-depth-6",
      "title": "lgbm, multiple classifier with max_depth=6",
      "source": "live",
      "kernel_id": "6480776"
    },
    {
      "ref": "ivakat/replacing-rusher-characteristics-with-1-number",
      "title": "Replacing rusher characteristics with 1 number",
      "source": "live",
      "kernel_id": "6480725"
    },
    {
      "ref": "jjs2434/nfl-big-bowl-data-iteration-for-good-model",
      "title": "NFL Big Bowl Data(Iteration for good model)",
      "source": "live",
      "kernel_id": "6480274"
    },
    {
      "ref": "otzhora/nfl-big-data-bowl",
      "title": "NFL Big Data Bowl",
      "source": "live",
      "kernel_id": "6479754"
    },
    {
      "ref": "coolcoder22/nfl-001-neural-network",
      "title": "[NFL] - [001]- [Neural Network]",
      "source": "live",
      "kernel_id": "6477619"
    },
    {
      "ref": "coolcoder22/nfl-001-random-forest",
      "title": "[NFL] - [001]- [Random Forest]",
      "source": "live",
      "kernel_id": "6477450"
    },
    {
      "ref": "sa0987/play-viz-modified-w-play-zoom-inset",
      "title": "Play viz  - Modified w/ play zoom inset ",
      "source": "live",
      "kernel_id": "6475369"
    },
    {
      "ref": "aleksthegreat/tfp-gaussian-distribution-nn",
      "title": "TFP + gaussian distribution NN",
      "source": "live",
      "kernel_id": "6474851"
    },
    {
      "ref": "mehrler/dataminingproject",
      "title": "DataMiningProject",
      "source": "live",
      "kernel_id": "6472728"
    },
    {
      "ref": "scirpus/fork-of-hybrid-gp-and-nn",
      "title": "Fork of Hybrid GP and NN",
      "source": "live",
      "kernel_id": "6470905"
    },
    {
      "ref": "mervesarac/voronoi-plays",
      "title": "voronoi_plays",
      "source": "live",
      "kernel_id": "6469063"
    },
    {
      "ref": "l0lbeattango3/voronoi-diagram-on-football-field-using-scipy",
      "title": "Voronoi diagram on Football Field Using Scipy",
      "source": "live",
      "kernel_id": "6464835"
    },
    {
      "ref": "yukomiya/komiya-nfl1-class-submit",
      "title": "Komiya_NFL1_class_submit",
      "source": "live",
      "kernel_id": "6464818"
    },
    {
      "ref": "funaki/fork-of-fork-of-fork-of-funaki-nfl-3",
      "title": "Fork of Fork of Fork of funaki_NFL_3",
      "source": "live",
      "kernel_id": "6464807"
    },
    {
      "ref": "funaki/fork-of-fork-of-funaki-nfl-3",
      "title": "Fork of Fork of funaki_NFL_3",
      "source": "live",
      "kernel_id": "6463949"
    },
    {
      "ref": "tuttifrutti/voronoi-diagram-in-python",
      "title": "Voronoi diagram in Python",
      "source": "live",
      "kernel_id": "6457463"
    },
    {
      "ref": "code1110/let-s-ask-catboost-about-cat-s",
      "title": "Let's ask CatBoost about cat(s)",
      "source": "live",
      "kernel_id": "6455901"
    },
    {
      "ref": "shubham2306/nfl-big-bowl-data-iteration-for-good-model",
      "title": "NFL Big Bowl Data(Iteration for good model)",
      "source": "live",
      "kernel_id": "6455358"
    },
    {
      "ref": "funaki/fork-of-funaki-nfl-3",
      "title": "Fork of funaki_NFL_3",
      "source": "live",
      "kernel_id": "6453069"
    },
    {
      "ref": "jasonrockphelps/convex-hull-feature-development",
      "title": "Convex Hull Feature Development",
      "source": "live",
      "kernel_id": "6450853"
    },
    {
      "ref": "gertjac/just-to-show-why-i-wonder-about-online-learning",
      "title": "just to show why I wonder about online learning",
      "source": "live",
      "kernel_id": "6450152"
    },
    {
      "ref": "gertjac/is-online-training-allowed",
      "title": "is online training allowed?",
      "source": "live",
      "kernel_id": "6449836"
    },
    {
      "ref": "justinehrlichsyr/justin-r-nfl-big-data-bowl",
      "title": "Justin R NFL Big Data Bowl",
      "source": "live",
      "kernel_id": "6449145"
    },
    {
      "ref": "axzhang/nn-no-player-specific-feature",
      "title": "NN-no-Player-Specific-Feature",
      "source": "live",
      "kernel_id": "6447549"
    },
    {
      "ref": "rodrigolima82/nfl-simple-eda",
      "title": "NFL Simple EDA",
      "source": "live",
      "kernel_id": "6444385"
    },
    {
      "ref": "ksayantani/understanding-variables",
      "title": "Understanding variables",
      "source": "live",
      "kernel_id": "6443189"
    },
    {
      "ref": "robertehshi/nfl-big-data-bowl-physics-model",
      "title": "NFL_Big_Data_Bowl_Physics_Model",
      "source": "live",
      "kernel_id": "6439066"
    },
    {
      "ref": "funaki/fork-of-funaki-nfl-2",
      "title": "Fork of funaki_NFL_2",
      "source": "live",
      "kernel_id": "6436880"
    },
    {
      "ref": "joocheol/qrm20191030",
      "title": "QRM20191030",
      "source": "live",
      "kernel_id": "6434870"
    },
    {
      "ref": "kenmatsu4/nn-outputs-gaussian-distribution-directly",
      "title": "NN outputs gaussian distribution directly.",
      "source": "live",
      "kernel_id": "6432822"
    },
    {
      "ref": "makrovmf3/nfl-04",
      "title": "NFL_04",
      "source": "live",
      "kernel_id": "6431100"
    },
    {
      "ref": "arihantkumarjain/nfl-preprocesisng-eda",
      "title": "NFL - Preprocesisng & EDA",
      "source": "live",
      "kernel_id": "6427033"
    },
    {
      "ref": "funaki/funaki-nfl-3",
      "title": "funaki_NFL_3",
      "source": "live",
      "kernel_id": "6426392"
    },
    {
      "ref": "ben519/understanding-x-y-dir-and-orientation",
      "title": "Understanding X, Y, Dir and Orientation",
      "source": "live",
      "kernel_id": "6421179"
    },
    {
      "ref": "sa0987/nfl-big-data-bowl-saman",
      "title": "NFL Big Data Bowl Saman",
      "source": "live",
      "kernel_id": "6419927"
    },
    {
      "ref": "manojprabhaakr/hybrid-gp-nn-with-feature-engineering",
      "title": "Hybrid GP NN with Feature Engineering",
      "source": "live",
      "kernel_id": "6417968"
    },
    {
      "ref": "yangsaewon/pandas",
      "title": "Pandas 병합 실습",
      "source": "live",
      "kernel_id": "6416122"
    },
    {
      "ref": "funaki/funaki-nfl-2",
      "title": "funaki_NFL_2",
      "source": "live",
      "kernel_id": "6412387"
    },
    {
      "ref": "ucfaibot/data-science-fa19-nfl-deep-dive",
      "title": "data-science-fa19-nfl-deep-dive",
      "source": "live"
    },
    {
      "ref": "muhakabartay/update-on-nn-multiple-output-stadium-clean",
      "title": "Update on \"NN, multiple output + stadium clean\"",
      "source": "live",
      "kernel_id": "6409125"
    },
    {
      "ref": "rooshroosh/continue-tune-hybrid-gp-and-nn",
      "title": "continue tune Hybrid GP and NN",
      "source": "live",
      "kernel_id": "6407081"
    },
    {
      "ref": "muhakabartay/update-of-hybrid-gp-and-nn",
      "title": "Update of \"Hybrid GP and NN\"",
      "source": "live",
      "kernel_id": "6401233"
    },
    {
      "ref": "datahobbit/hybrid-gp-and-nn-slightly-tuned",
      "title": "Hybrid GP and NN slightly tuned",
      "source": "live",
      "kernel_id": "6399087"
    },
    {
      "ref": "scirpus/hybrid-gp-and-nn",
      "title": "Hybrid GP and NN",
      "source": "live",
      "kernel_id": "6396023"
    },
    {
      "ref": "scirpus/location-eda",
      "title": "Location EDA",
      "source": "live",
      "kernel_id": "6395603"
    },
    {
      "ref": "threecourse/initial-wrangling-voronoi-areas-in-python",
      "title": "initial wrangling & Voronoi areas in Python",
      "source": "live",
      "kernel_id": "6395332"
    },
    {
      "ref": "bestpredict/location-eda-8eb410",
      "title": "Location EDA 8eb410",
      "source": "live",
      "kernel_id": "6394137"
    },
    {
      "ref": "rx74tsns/simple-nn",
      "title": "Simple NN",
      "source": "live",
      "kernel_id": "6392522"
    },
    {
      "ref": "fallen123/nfl-big-data-bowl",
      "title": "NFL Big Data Bowl",
      "source": "live",
      "kernel_id": "6388356"
    },
    {
      "ref": "nicodre/nfl-comp-v3",
      "title": "NFL comp v3",
      "source": "live",
      "kernel_id": "6385732"
    },
    {
      "ref": "ryches/cnn-on-the-playing-field",
      "title": "CNN on the playing field",
      "source": "live",
      "kernel_id": "6381023"
    },
    {
      "ref": "kyonaganuma/hierarchical-bayesian-modeling-using-pystan",
      "title": "Hierarchical Bayesian Modeling using pystan",
      "source": "live",
      "kernel_id": "6380746"
    },
    {
      "ref": "yukomiya/komiya-nfl1",
      "title": "Komiya_NFL1",
      "source": "live",
      "kernel_id": "6380700"
    },
    {
      "ref": "funaki/funaki-nfl",
      "title": "funaki_NFL",
      "source": "live",
      "kernel_id": "6380682"
    },
    {
      "ref": "jmaslek/nflbowl-nearestdefender-myfirstnn",
      "title": "NFLBOWL_nearestdefender_myfirstNN",
      "source": "live",
      "kernel_id": "6378188"
    },
    {
      "ref": "baghirli/nfl-big-data-bowl-keras-nn-pooling-resnet",
      "title": "NFL Big Data Bowl - KERAS NN + Pooling + ResNet",
      "source": "live",
      "kernel_id": "6376942"
    },
    {
      "ref": "christoffer/how-good-are-our-predictions-really",
      "title": "How good are our predictions really?",
      "source": "live",
      "kernel_id": "6373862"
    },
    {
      "ref": "hookbook/plays-visualization-with-pitch-control",
      "title": "Plays Visualization with Pitch Control",
      "source": "live",
      "kernel_id": "6369821"
    },
    {
      "ref": "rutviklathiya/nfl-starter-mlp-feature-engg",
      "title": "NFL Starter. MLP + Feature Engg. ",
      "source": "live",
      "kernel_id": "6355188"
    },
    {
      "ref": "ananthreddy/big-data-bowl",
      "title": "Big Data Bowl",
      "source": "live",
      "kernel_id": "6345538"
    },
    {
      "ref": "pednt9/vip-hint-coded",
      "title": "VIP Hint coded",
      "source": "live",
      "kernel_id": "6343029"
    },
    {
      "ref": "manish25297/nfl-league",
      "title": "nfl_league",
      "source": "live",
      "kernel_id": "6339123"
    },
    {
      "ref": "gpreda/accelerated-data-analytics-using-pandas-profiling",
      "title": "Accelerated Data Analytics using Pandas-Profiling",
      "source": "live",
      "kernel_id": "6333255"
    },
    {
      "ref": "mrkmakr/lgbm-multiple-classifier",
      "title": "lgbm, multiple classifier ",
      "source": "live",
      "kernel_id": "6332915"
    },
    {
      "ref": "youwbpyr/nfl-test-data-for-local-devlop",
      "title": "NFL test data for local devlop",
      "source": "live",
      "kernel_id": "6331289"
    },
    {
      "ref": "fattycat/nfl-big-data-bowl-official-starter-notebook",
      "title": "NFL Big Data Bowl Official Starter Notebook",
      "source": "live",
      "kernel_id": "6329050"
    },
    {
      "ref": "truenikita/neural-networks-multiple-output-stadium-clean",
      "title": "Neural networks, multiple output + stadium clean",
      "source": "live",
      "kernel_id": "6324522"
    },
    {
      "ref": "agenlu/r-starter-notebook-with-20x-faster-data-table",
      "title": "R Starter Notebook with 20x Faster data table",
      "source": "live",
      "kernel_id": "6322628"
    },
    {
      "ref": "mrkmakr/neural-network-with-mae-objective-0-01381",
      "title": "neural network, with mae objective, 0.01381",
      "source": "live",
      "kernel_id": "6319549"
    },
    {
      "ref": "freelancerfel/utility-nflcleaning",
      "title": "Utility - NFLCleaning",
      "source": "live",
      "kernel_id": "6317668"
    },
    {
      "ref": "robikscube/the-race-for-nfl-big-data-bowl-2020",
      "title": "🏎️ 🏈 The Race for NFL Big Data Bowl 2020",
      "source": "live",
      "kernel_id": "6314394"
    },
    {
      "ref": "dmitriyguller/r-starter-notebook-with-15x-faster-prediction-loop",
      "title": "R Starter Notebook with 15x Faster Prediction Loop",
      "source": "live",
      "kernel_id": "6312753"
    },
    {
      "ref": "aerdem4/nfl-lofo-importance",
      "title": "NFL LOFO Importance",
      "source": "live",
      "kernel_id": "6310999"
    },
    {
      "ref": "heye0507/eda-demo",
      "title": "EDA_demo",
      "source": "live",
      "kernel_id": "6309398"
    },
    {
      "ref": "puneetgrover/ensembling-nns-and-lightgbm",
      "title": "Ensembling NNs and LightGBM",
      "source": "live",
      "kernel_id": "6308148"
    },
    {
      "ref": "govoruha/r-simple-lgb-starter",
      "title": "R Simple lgb starter",
      "source": "live",
      "kernel_id": "6305785"
    },
    {
      "ref": "adale2016/nfl-big-data-bowl-official-starter-notebook",
      "title": "NFL Big Data Bowl Official Starter Notebook",
      "source": "live",
      "kernel_id": "6300436"
    },
    {
      "ref": "tlegrand/end-to-end-sklearn-pipeline-with-feature-cleaning",
      "title": "End-to-end Sklearn Pipeline with feature cleaning",
      "source": "live",
      "kernel_id": "6295115"
    },
    {
      "ref": "steubk/simulate-iter-test-on-train",
      "title": "Simulate iter_test on train",
      "source": "live",
      "kernel_id": "6292333"
    },
    {
      "ref": "joocheol/fe20191019",
      "title": "FE20191019",
      "source": "live",
      "kernel_id": "6285502"
    },
    {
      "ref": "peterhurford/something-is-the-matter-with-orientation",
      "title": "Something is the Matter with Orientation",
      "source": "live",
      "kernel_id": "6282555"
    },
    {
      "ref": "zlindsey/nfl-data-cleaning-and-regression",
      "title": "NFL Data Cleaning and Regression",
      "source": "live",
      "kernel_id": "6281853"
    },
    {
      "ref": "kenmatsu4/animation-of-field-data-player-position-yards",
      "title": "Animation of field data (Player position & Yards)",
      "source": "live",
      "kernel_id": "6281438"
    },
    {
      "ref": "gopidurgaprasad/feature-by-feature-explained-in-detailed",
      "title": "Feature by Feature Explained in detailed ",
      "source": "live",
      "kernel_id": "6280235"
    },
    {
      "ref": "eristoddle/fork-of-neural-networks-different-2-comm",
      "title": "fork of neural networks + different 2 comm",
      "source": "live"
    },
    {
      "ref": "errolpereira/fork-of-starter-submission",
      "title": "Fork of Starter Submission",
      "source": "live",
      "kernel_id": "6274763"
    },
    {
      "ref": "jy199412/18th-of-october",
      "title": "18th of October",
      "source": "live",
      "kernel_id": "6273676"
    },
    {
      "ref": "hookbook/nfl-data-visualization-of-plays",
      "title": "NFL Data: Visualization of Plays",
      "source": "live",
      "kernel_id": "6271279"
    },
    {
      "ref": "takaishikawa/very-newbie-notebook",
      "title": "Very Newbie Notebook",
      "source": "live",
      "kernel_id": "6271102"
    },
    {
      "ref": "truenikita/very-newbie-notebook",
      "title": "Very Newbie Notebook",
      "source": "live",
      "kernel_id": "6269579"
    },
    {
      "ref": "ucfaibot/data-science-fa19-nfl-intro",
      "title": "data-science-fa19-nfl-intro",
      "source": "live"
    },
    {
      "ref": "truenikita/try-of-nfl-id-binarization",
      "title": "Try of NFL ID binarization",
      "source": "live",
      "kernel_id": "6265846"
    },
    {
      "ref": "sryo188558/cox-proportional-hazard-model",
      "title": "Cox proportional hazard model",
      "source": "live",
      "kernel_id": "6265806"
    },
    {
      "ref": "nivanovi/nfl-lgbm",
      "title": "NFL LGBM",
      "source": "live",
      "kernel_id": "6265490"
    },
    {
      "ref": "hatunina/nfl-simple-pytorch-by-16-feats",
      "title": "NFL Simple PyTorch by 16 feats",
      "source": "live",
      "kernel_id": "6264138"
    },
    {
      "ref": "ben519/empirical-cdfs-benchmark",
      "title": "Empirical CDFs Benchmark",
      "source": "live",
      "kernel_id": "6263719"
    },
    {
      "ref": "ajsteele/nfl-big-data-bowl-highlighting-line-of-scrimmage",
      "title": "NFL Big Data Bowl - Highlighting Line of Scrimmage",
      "source": "live",
      "kernel_id": "6262953"
    },
    {
      "ref": "adale2016/nfl-1-0",
      "title": "NFL 1.0",
      "source": "live",
      "kernel_id": "6252864"
    },
    {
      "ref": "wcukierski/official-r-starter-notebook-nfl-big-data-bowl",
      "title": "Official R Starter Notebook - NFL Big Data Bowl",
      "source": "live",
      "kernel_id": "6252118"
    },
    {
      "ref": "truenikita/cleaning-of-the-stadium-feature",
      "title": "Cleaning of the \"Stadium\" feature",
      "source": "live",
      "kernel_id": "6251291"
    },
    {
      "ref": "donkeys/keras-multi-input",
      "title": "Keras Multi-Input",
      "source": "live",
      "kernel_id": "6250741"
    },
    {
      "ref": "miklgr500/fork-neural-networks-adversarial-validation",
      "title": "fork neural networks... Adversarial Validation",
      "source": "live",
      "kernel_id": "6248333"
    },
    {
      "ref": "rooshroosh/fork-of-neural-networks-different-architecture",
      "title": "fork of neural networks + different architecture",
      "source": "live",
      "kernel_id": "6247215"
    },
    {
      "ref": "econjt/first-submission",
      "title": "First_submission ",
      "source": "live",
      "kernel_id": "6241953"
    },
    {
      "ref": "econjt/simple-regression-first-submission",
      "title": "Simple Regression(First Submission)",
      "source": "live",
      "kernel_id": "6241406"
    },
    {
      "ref": "joocheol/20191017",
      "title": "20191017",
      "source": "live",
      "kernel_id": "6240212"
    },
    {
      "ref": "mlfurman/plotting-field-and-plays-in-r",
      "title": "Plotting field and plays in R",
      "source": "live",
      "kernel_id": "6237754"
    },
    {
      "ref": "tnmasui/standardizing-s-by-seasons",
      "title": "Standardizing S by Seasons",
      "source": "live",
      "kernel_id": "6237062"
    },
    {
      "ref": "arindam235/andy-s-draft",
      "title": "Andy's Draft",
      "source": "live",
      "kernel_id": "6232013"
    },
    {
      "ref": "rdboyes/simple-voronoi-polygon-visualization-in-r",
      "title": "Simple Voronoi Polygon Visualization in R ",
      "source": "live",
      "kernel_id": "6230521"
    },
    {
      "ref": "hamditarek/squad-visualization-xy-coordinate",
      "title": "Squad Visualization (XY Coordinate)",
      "source": "live",
      "kernel_id": "6221009"
    },
    {
      "ref": "evazhang43/nfl-eda",
      "title": "NFL EDA",
      "source": "live",
      "kernel_id": "6217129"
    },
    {
      "ref": "cpmpml/initial-wrangling-voronoi-areas-in-python",
      "title": "initial wrangling & Voronoi areas in Python",
      "source": "live",
      "kernel_id": "6216184"
    },
    {
      "ref": "davidcairuz/nfl-neural-network-w-softmax",
      "title": "NFL Neural Network w/ Softmax",
      "source": "live",
      "kernel_id": "6215617"
    },
    {
      "ref": "donghoonoh/2018195017-qrm",
      "title": "2018195017 QRM",
      "source": "live",
      "kernel_id": "6214790"
    },
    {
      "ref": "thehemen/nn-feature-engineering-with-genetic-programming",
      "title": "NN + Feature Engineering with Genetic Programming ",
      "source": "live",
      "kernel_id": "6213396"
    },
    {
      "ref": "muxiaobai/my-npl-submit-demo",
      "title": "my-NPL-submit-demo",
      "source": "live",
      "kernel_id": "6210969"
    },
    {
      "ref": "snakayama/nfl-interactive-graph-using-bokeh",
      "title": "NFL interactive graph using bokeh",
      "source": "live",
      "kernel_id": "6208969"
    },
    {
      "ref": "tunguz/adversarial-nfl",
      "title": "Adversarial NFL",
      "source": "live",
      "kernel_id": "6206638"
    },
    {
      "ref": "carlossouza/model-by-runner",
      "title": "Model by runner?",
      "source": "live",
      "kernel_id": "6205331"
    },
    {
      "ref": "ryancaldwell/location-eda",
      "title": "Location EDA",
      "source": "live",
      "kernel_id": "6204649"
    },
    {
      "ref": "akashii99/nfl-analysis",
      "title": "NFL Analysis",
      "source": "live",
      "kernel_id": "6202192"
    },
    {
      "ref": "hamditarek/fork-of-neural-networks-feature-luck-computer",
      "title": "fork of neural networks + feature luck computer?",
      "source": "live",
      "kernel_id": "6201911"
    },
    {
      "ref": "s903124/ngboost-for-nfl-data",
      "title": "NGBoost for NFL data",
      "source": "live",
      "kernel_id": "6201075"
    },
    {
      "ref": "tnmasui/nfl-eda-on-train-test-fe-correlation-anova",
      "title": "NFL EDA on Train & Test / FE, Correlation, ANOVA",
      "source": "live",
      "kernel_id": "6198522"
    },
    {
      "ref": "etsc9287/nfl-2018-eda-and-feature-engineering",
      "title": "🏈 NFL 2018 EDA and Feature Engineering 🏈",
      "source": "live",
      "kernel_id": "6198359"
    },
    {
      "ref": "prashantkikani/nfl-starter-mlp-feature-engg",
      "title": "NFL Starter. MLP + Feature Engg. ",
      "source": "live",
      "kernel_id": "6198111"
    },
    {
      "ref": "kingychiu/keras-nn-starter-crps-early-stopping",
      "title": "🏈 Keras NN Starter &  CRPS & Early Stopping 🏈",
      "source": "live",
      "kernel_id": "6197377"
    },
    {
      "ref": "kusunokichihiro/description-in-japanese-and-first-model-example",
      "title": "Description in Japanese and First Model Example",
      "source": "live",
      "kernel_id": "6196730"
    },
    {
      "ref": "statnikmusic/establishing-the-visualization-of-the-run",
      "title": "Establishing (the Visualization of) the Run ",
      "source": "live",
      "kernel_id": "6196486"
    },
    {
      "ref": "kashnitsky/nfl-median-baseline-0-01457",
      "title": "NFL median baseline (0.01457)",
      "source": "live",
      "kernel_id": "6195988"
    },
    {
      "ref": "zlindsey/nfl-eda-1",
      "title": "NFL EDA 1",
      "source": "live",
      "kernel_id": "6195617"
    },
    {
      "ref": "adam1brownell/visualizing-plays-and-a-simple-proximity-nn",
      "title": "Visualizing Plays (and a simple proximity NN)",
      "source": "live",
      "kernel_id": "6195605"
    },
    {
      "ref": "jpmiller/simple-distribution",
      "title": "Simple Distribution",
      "source": "live",
      "kernel_id": "6194965"
    },
    {
      "ref": "fatihbilgin/nfl-big-data-visualization",
      "title": "NFL Big Data Visualization",
      "source": "live",
      "kernel_id": "6194929"
    },
    {
      "ref": "miklgr500/tabular-data-image",
      "title": "Tabular data - > Image",
      "source": "live",
      "kernel_id": "6194389"
    },
    {
      "ref": "iaarod/nfl-data-visualizing-acceleration",
      "title": "NFL Data: Visualizing Acceleration",
      "source": "live",
      "kernel_id": "6193615"
    },
    {
      "ref": "hamditarek/neural-networks-feature-engineering-for-the-win",
      "title": "neural networks + feature engineering for the win",
      "source": "live",
      "kernel_id": "6193495"
    },
    {
      "ref": "miklgr500/fork-of-neural-networks-radam-repeatkfold",
      "title": "fork of neural networks... + RAdam & RepeatKfold",
      "source": "live",
      "kernel_id": "6192291"
    },
    {
      "ref": "miliman13/nfl-plotting-play-situation",
      "title": "NFL Plotting Play Situation",
      "source": "live",
      "kernel_id": "6191368"
    },
    {
      "ref": "tarunpaparaju/nfl-competition-eda-pytorch-cnn",
      "title": "NFL Competition : EDA + PyTorch CNN 🔥",
      "source": "live",
      "kernel_id": "6189041"
    },
    {
      "ref": "xwxw2929/nn-kfold",
      "title": "NN+KFold",
      "source": "live",
      "kernel_id": "6188586"
    },
    {
      "ref": "kunqi77/eda-all-columns",
      "title": "EDA (all columns)",
      "source": "live",
      "kernel_id": "6187549"
    },
    {
      "ref": "lovedm/fork-of-neural-networks-feature-luck-computer",
      "title": "fork of neural networks + feature luck computer?",
      "source": "live",
      "kernel_id": "6185891"
    },
    {
      "ref": "alexanderdbooth/nfl-big-data-bowl-wooooo",
      "title": "NFL Big Data Bowl wooooo",
      "source": "live",
      "kernel_id": "6184822"
    },
    {
      "ref": "mconway/sarimax",
      "title": "SARIMAX",
      "source": "live",
      "kernel_id": "6184736"
    },
    {
      "ref": "scirpus/gradbm11",
      "title": "GradBM11",
      "source": "live",
      "kernel_id": "6182361"
    },
    {
      "ref": "rdizzl3/nfl-big-data-bowl-classification-perspective",
      "title": "NFL Big Data Bowl - Classification Perspective ",
      "source": "live",
      "kernel_id": "6181868"
    },
    {
      "ref": "btatkinson/corrected-orientation-dir-vectors-python",
      "title": "[Corrected] Orientation/Dir Vectors Python",
      "source": "live",
      "kernel_id": "6180574"
    },
    {
      "ref": "jaseziv83/comprehensive-cleaning-and-eda-of-all-variables",
      "title": "Comprehensive Cleaning and EDA of ALL Variables",
      "source": "live",
      "kernel_id": "6179076"
    },
    {
      "ref": "nikhil04/nfl-big-data",
      "title": "NFL Big Data",
      "source": "live",
      "kernel_id": "6176024"
    },
    {
      "ref": "ekrembayar/nfl-big-data-bowl-formations-visualization",
      "title": "NFL Big Data Bowl Formations Visualization",
      "source": "live",
      "kernel_id": "6175624"
    },
    {
      "ref": "ryches/model-free-benchmark",
      "title": "Model-Free Benchmark",
      "source": "live",
      "kernel_id": "6175261"
    },
    {
      "ref": "snehalsandri007/kernel1e0cf41817",
      "title": "kernel1e0cf41817",
      "source": "live",
      "kernel_id": "6174923"
    },
    {
      "ref": "ryches/nfl-feature-engineering-v2",
      "title": "NFL Feature Engineering v2",
      "source": "live",
      "kernel_id": "6174531"
    },
    {
      "ref": "ankittomar1/nbl-pandas-profiling",
      "title": "NBL_Pandas-profiling",
      "source": "live",
      "kernel_id": "6173683"
    },
    {
      "ref": "shahules/how-about-some-nn-keras-starter",
      "title": "How about some NN? Keras Starter",
      "source": "live",
      "kernel_id": "6173271"
    },
    {
      "ref": "hukuda222/nfl-simple-evluation-trick",
      "title": "NFL simple evluation trick",
      "source": "live",
      "kernel_id": "6173073"
    },
    {
      "ref": "bensdm/eda-profile-report",
      "title": "EDA profile report",
      "source": "live",
      "kernel_id": "6172967"
    },
    {
      "ref": "statsbymichaellopez/nfl-tracking-wrangling-voronoi-and-sonars",
      "title": "NFL tracking: wrangling, Voronoi, and sonars",
      "source": "live",
      "kernel_id": "6172864"
    },
    {
      "ref": "prashantkikani/nfl-starter-lgb-feature-engg",
      "title": "NFL Starter. LGB + Feature Engg. ",
      "source": "live",
      "kernel_id": "6172840"
    },
    {
      "ref": "zmuscarella/visualization-directional-rushing",
      "title": "Visualization: Directional Rushing",
      "source": "live",
      "kernel_id": "6172782"
    },
    {
      "ref": "bbissey/nfl-big-data-bowl-official-starter-notebook",
      "title": "NFL Big Data Bowl Official Starter Notebook",
      "source": "live",
      "kernel_id": "6171492"
    },
    {
      "ref": "truenikita/gradbm11",
      "title": "GradBM11",
      "source": "live",
      "kernel_id": "6169335"
    },
    {
      "ref": "bgmello/neural-networks-feature-engineering-for-the-win",
      "title": "neural networks + feature engineering for the win",
      "source": "live",
      "kernel_id": "6164308"
    },
    {
      "ref": "somnambwl/nfl-basic-player-information",
      "title": "NFL - Basic Player Information",
      "source": "live",
      "kernel_id": "6164287"
    },
    {
      "ref": "gunesevitan/nfl-big-data-bowl-test-set",
      "title": "NFL Big Data Bowl - Test Set",
      "source": "live",
      "kernel_id": "6164058"
    },
    {
      "ref": "jesucristo/animated-visualization",
      "title": "🏈 Animated Visualization",
      "source": "live",
      "kernel_id": "6161798"
    },
    {
      "ref": "adibpriatama/pipeline-design-simple",
      "title": "Pipeline_design_simple",
      "source": "live",
      "kernel_id": "6161155"
    },
    {
      "ref": "pradeepmuniasamy/extensive-eda-fe-models-on-nfl-big-data-bowl",
      "title": "Extensive EDA +FE+Models on NFL Big Data Bowl",
      "source": "live",
      "kernel_id": "6159631"
    },
    {
      "ref": "sudalairajkumar/simple-exploration-notebook-nfl",
      "title": "Simple Exploration Notebook - NFL",
      "source": "live",
      "kernel_id": "6159428"
    },
    {
      "ref": "ryches/nfl-big-data-bowl-team-feature-simple-model",
      "title": "NFL Big Data Bowl Team Feature Simple Model",
      "source": "live",
      "kernel_id": "6158596"
    },
    {
      "ref": "chabir/eda-3-lines",
      "title": "EDA - 3 lines",
      "source": "live",
      "kernel_id": "6157963"
    },
    {
      "ref": "code1110/eda-optimizing-lightgbm-hyperparameters",
      "title": "EDA & optimizing lightGBM hyperparameters",
      "source": "live",
      "kernel_id": "6157597"
    },
    {
      "ref": "subinium/i-want-to-see-all-features-eda-visualization",
      "title": "I Want to See All Features 🏈 (eda&visualization)",
      "source": "live",
      "kernel_id": "6157276"
    },
    {
      "ref": "jaseziv83/identifying-data-issues-training-set",
      "title": "Identifying Data Issues Training Set",
      "source": "live",
      "kernel_id": "6157228"
    },
    {
      "ref": "jastornaut/nfl-eda-ftw-lean-clean-and-astroturf-green",
      "title": "NFL EDA FTW - Lean, Clean, and AstroTurf Green",
      "source": "live",
      "kernel_id": "6157034"
    },
    {
      "ref": "ksooklall/nfl-bdb-eda",
      "title": "NFL BDB: EDA",
      "source": "live",
      "kernel_id": "6156985"
    },
    {
      "ref": "hamditarek/eda-to-prediction-features-analysis-engineering",
      "title": "EDA To Prediction: Features analysis & Engineering",
      "source": "live",
      "kernel_id": "6156976"
    },
    {
      "ref": "hukuda222/nfl-simple-model-using-lightgbm",
      "title": "NFL simple model using lightGBM",
      "source": "live",
      "kernel_id": "6156819"
    },
    {
      "ref": "robikscube/big-data-bowl-comprehensive-eda-with-pandas",
      "title": "🏟️ Big Data Bowl - Comprehensive EDA with Pandas",
      "source": "live",
      "kernel_id": "6156815"
    },
    {
      "ref": "sinchir0/nfl-simple-eda",
      "title": "NFL - Simple EDA",
      "source": "live",
      "kernel_id": "6156715"
    },
    {
      "ref": "robikscube/nfl-big-data-bowl-plotting-player-position",
      "title": "NFL Big Data Bowl - Plotting Player Position",
      "source": "live",
      "kernel_id": "6156489"
    },
    {
      "ref": "dster/nfl-big-data-bowl-official-starter-notebook",
      "title": "NFL Big Data Bowl Official Starter Notebook",
      "source": "live",
      "kernel_id": "6156173"
    },
    {
      "ref": "glenn123/glenn-16-10-2019",
      "title": "",
      "source": "meta",
      "kernel_id": "6241991"
    },
    {
      "ref": "s903124/stability-of-running-back-performance",
      "title": "",
      "source": "meta",
      "kernel_id": "6818989"
    },
    {
      "ref": "maxisoft/install-packed-package",
      "title": "",
      "source": "meta",
      "kernel_id": "6164025"
    },
    {
      "ref": "connerevans/big-data-bowl-2023-linemen-metrics",
      "title": "",
      "source": "meta",
      "kernel_id": "33501219"
    },
    {
      "ref": "ddhannah/nfl-big-data",
      "title": "",
      "source": "meta",
      "kernel_id": "33052939"
    },
    {
      "ref": "theholycityweb/data-science-fa19-nfl-intro",
      "title": "",
      "source": "meta",
      "kernel_id": "36641963"
    },
    {
      "ref": "ryanmailley/evaluating-tackles-by-level-of-defense",
      "title": "",
      "source": "meta",
      "kernel_id": "49025404"
    },
    {
      "ref": "fernandosr85/snap-insights-beyond-the-obvious",
      "title": "",
      "source": "meta",
      "kernel_id": "67735262"
    },
    {
      "ref": "humeyracimen/global-ai-hub-aygaz-veri-analizi-bootcamp",
      "title": "",
      "source": "meta",
      "kernel_id": "71157458"
    },
    {
      "ref": "animeshjoshi475/pass-utility-optimization-via-utility-functions",
      "title": "",
      "source": "meta",
      "kernel_id": "74191094"
    },
    {
      "ref": "bakhtiyoralimov/dent-defensive-entropy-in-the-nfl",
      "title": "",
      "source": "meta",
      "kernel_id": "74108192"
    },
    {
      "ref": "calebbradberry/simple-voronoi-tesselations",
      "title": "",
      "source": "meta",
      "kernel_id": "74100990"
    }
  ],
  "errors": []
}