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      "ref": "fernoklump/optimum-variables-w-tuned-extratrees",
      "votes": 1
    },
    {
      "ref": "fernoklump/final-vars-with-tuned-variables",
      "votes": 0
    },
    {
      "ref": "paban077/forest-cover-type-prediction-simple-model",
      "votes": 4
    },
    {
      "ref": "willtscott/forest-exploration-and-prediction",
      "votes": 0
    },
    {
      "ref": "shahules/forest-cover-type-edta-ensemble-voting",
      "votes": 10
    },
    {
      "ref": "bigkirill/forest-cover-using-catboost-multiclass-classifier",
      "votes": 32
    },
    {
      "ref": "mlisovyi/class-fractions-in-the-test",
      "votes": 0
    },
    {
      "ref": "suyashgulati/using-pyspark-randomforest-crossvalidatn-gridsrch",
      "votes": 101
    },
    {
      "ref": "mlisovyi/hyper-parameter-optimisation",
      "votes": 1
    },
    {
      "ref": "sassy19a/exploratory-data-analysis-forest-cover",
      "votes": 13
    },
    {
      "ref": "arvindbhatt/forest-cover-type",
      "votes": 1
    },
    {
      "ref": "sassy19a/random-forest-forest-cover",
      "votes": 8
    },
    {
      "ref": "mik3hall/forest-cover-deja-vu",
      "votes": 0
    },
    {
      "ref": "syncush/neural-forest-and-voting-79-accuracy",
      "votes": 0
    },
    {
      "ref": "schlerp/eda-fe-rfe-ann-blending-of-classifiers",
      "votes": 6
    },
    {
      "ref": "manufacturingai/forestcovertype-with-tensorflow-and-keras",
      "votes": 4
    },
    {
      "ref": "mlisovyi/featureengineering-training-with-weights",
      "votes": 18
    },
    {
      "ref": "plarmuseau/3y-old-solution-litterary",
      "votes": 10
    },
    {
      "ref": "cbryant/new-features-and-recursive-feature-extraction",
      "votes": 8
    },
    {
      "ref": "pstetz/eda-of-forest-cover-with-plotly-and-altair",
      "votes": 8
    },
    {
      "ref": "abusiddik/forestcovertype",
      "votes": 3
    },
    {
      "ref": "plarmuseau/anyone-an-idea-about-this-cluster",
      "votes": 1
    },
    {
      "ref": "inmanenz/keras-ann-with-some-text-pre-processing",
      "votes": 2
    },
    {
      "ref": "ashishpatel26/bayesian-random-forest-lightgbm",
      "votes": 13
    },
    {
      "ref": "dshobana9/kernel1ec3c94271",
      "votes": 0
    },
    {
      "ref": "dshobana9/forest-kernel",
      "votes": 0
    },
    {
      "ref": "suyashgulati/2-anns-layers-based-on-features-importance",
      "votes": 3
    },
    {
      "ref": "schlerp/ga-evolution-for-ann-as-fe-into-sklearn-classifier",
      "votes": 1
    },
    {
      "ref": "tarunpaparaju/the-forest-cover-type-predictor",
      "votes": 2
    },
    {
      "ref": "tarunpaparaju/forest-cover-type-predictor",
      "votes": 4
    },
    {
      "ref": "aks1nya/logistic-reg-analysis",
      "votes": 0
    },
    {
      "ref": "atrisaxena/forest-cover-voted-classifier-using-mod",
      "votes": 5
    },
    {
      "ref": "elitcohen/forest-cover-type-eda-modeling-error-analysis",
      "votes": 31
    },
    {
      "ref": "diceke/my-submission",
      "votes": 1
    },
    {
      "ref": "henryhollis4/kernel9740dc77d9",
      "votes": 2
    },
    {
      "ref": "puneettyagi/forest-cover-simple-xgb",
      "votes": 2
    },
    {
      "ref": "nafisur/voting-classifier-for-victory",
      "votes": 4
    },
    {
      "ref": "alfar9000/forest-cover-type-exploratory",
      "votes": 0
    },
    {
      "ref": "schlerp/simple-ann-trained-with-ga",
      "votes": 0
    },
    {
      "ref": "thebrownviking20/voting-classifier-for-victory",
      "votes": 38
    },
    {
      "ref": "viswajithkn/second-attempt",
      "votes": 0
    },
    {
      "ref": "viswajithkn/notebook-submission",
      "votes": 1
    },
    {
      "ref": "viswajithkn/first-submission-forest",
      "votes": 0
    },
    {
      "ref": "ariadneadler/stacked-classifiers-0-818",
      "votes": 16
    },
    {
      "ref": "rohandx1996/features-hybrid-classifier-pls-vote-if-u-like",
      "votes": 42
    },
    {
      "ref": "schlerp/custom-numpy-neural-network",
      "votes": 3
    },
    {
      "ref": "dimonrtm/randomforestclassifierandextratreesclassifier",
      "votes": 3
    },
    {
      "ref": "gafalcon/tuning-xgboost",
      "votes": 1
    },
    {
      "ref": "pecooper/i-can-t-see-the-wood-for-the-trees",
      "votes": 0
    },
    {
      "ref": "hancelpv/forest-cover-type-classifier",
      "votes": 2
    },
    {
      "ref": "dimonrtm/xgboostclassifier",
      "votes": 1
    },
    {
      "ref": "bathiamkwrdsbt/forestcovertype-adaboost-dtree",
      "votes": 0
    },
    {
      "ref": "sumedhgodbole/ann-forest-cover-type-kernel-2",
      "votes": 0
    },
    {
      "ref": "moghazy/ensemble-learning-with-feature-engineering",
      "votes": 106
    },
    {
      "ref": "schlerp/keras-ann-kfold-ensemble-normalisation-fe",
      "votes": 1
    },
    {
      "ref": "schlerp/lgb-cv-ensemble-normalisation-fe",
      "votes": 4
    },
    {
      "ref": "schlerp/knn-simple-but-effective",
      "votes": 2
    },
    {
      "ref": "rohandx1996/eda-and-feature-selection",
      "votes": 41
    },
    {
      "ref": "dimonrtm/fneiralnetwork",
      "votes": 0
    },
    {
      "ref": "smehta12/forest-cover-type",
      "votes": 1
    },
    {
      "ref": "tejaeduc/foresttype-using-keras-nn",
      "votes": 3
    },
    {
      "ref": "liamjflood/forest-class-1",
      "votes": 0
    },
    {
      "ref": "dimonrtm/first",
      "votes": 0
    },
    {
      "ref": "dimonrtm/firstneiralnetwok",
      "votes": 4
    },
    {
      "ref": "mohamedramzy/xgboost-classifier-for-identifying-forests-covers",
      "votes": 2
    },
    {
      "ref": "zhoulingyan0228/eda-and-dnn-on-forest-coverage",
      "votes": 2
    },
    {
      "ref": "sabarishg/forest-cover-type",
      "votes": 1
    },
    {
      "ref": "qgh1223/kernel4d3cf44d34",
      "votes": 0
    },
    {
      "ref": "nathra/strong-structure-in-the-id-column",
      "votes": 0
    },
    {
      "ref": "adityajn105/forest-cover-type-prediction",
      "votes": 2
    },
    {
      "ref": "romankovalenko/data-distribution-3d-scatter-plots",
      "votes": 6
    },
    {
      "ref": "thebrownviking20/ann-starter",
      "votes": 35
    },
    {
      "ref": "skillsmuggler/forest-cover-feature-engineering-and-reduction",
      "votes": 15
    },
    {
      "ref": "indranilbhattacharya/xgb-parameter-tuning-from-scratch-random-search",
      "votes": 18
    },
    {
      "ref": "nathra/lightgbm-model-with-some-feature-engineering",
      "votes": 0
    },
    {
      "ref": "nathra/keras-deep-nn-with-text-features-upsampling",
      "votes": 9
    },
    {
      "ref": "artgor/forest-exploration-and-trees",
      "votes": 15
    },
    {
      "ref": "codename007/forest-cover-type-eda-baseline-model",
      "votes": 147
    },
    {
      "ref": "skillsmuggler/eda-and-dimension-reduction",
      "votes": 11
    },
    {
      "ref": "ambarish/forest-cover-type-eda-and-modelling",
      "votes": 14
    },
    {
      "ref": "nicapotato/multi-class-lgbm-cv-and-seed-diversification",
      "votes": 84
    }
  ]
}