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Community contributed notebooks to help you get started right away-
What about constant solution??? Selection of constants based on the distribution of test data and 0.806 on the leaderboardsweetlhare· Over 2 years ago -
R you normal? (explore datapoints + xgboost training 0.606) Challenges reflections and r xgboost approachdemarsylvain· Over 2 years ago -
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F1:0.376 Image pixel representation + CNN Model Baseline Create Image-Pixel like representation features and Convolution Neural Network based baselinenilabha· Over 2 years ago -
Detailed Data Analysis & Simple CatBoost - 0.640 on LB Description of features and the entire dataset, selection of categorical features by logic, CatBoostsweetlhare· Over 2 years ago -
undersampling+bagging example To tackle with huge class imbalance, I applied downsampling+bagging using lightgbm.jsato· Over 2 years ago -
F1:0.52-Baseline Imbalance Samplers(20+) and 8Classifiers Automated Benchmark of Imbalanced Samplers and Classifiers + Feature Engineering with Shapley Valuesnilabha· Over 2 years ago -
EDA, FE, HPO - All you need (LB: 0.640) Detailed EDA, FE with Class Balancing, Hyper-Parameter Optimization of XGBoost using Optunajyot_makadiya· Over 2 years ago -
What about neural networks? (0.694 LogLoss , 0.497 F1) Trying out neural networks, imputing NaN values with KNNs, and exploring the data!mmi333· Over 2 years ago -
End-to-End Simple Solution (9 Models + Data Imbalance) In this simple notebook I provide an End-to-End Solution with 9 models + data imbalance optionssantiactis· Over 2 years ago -
Dealing with Class Imbalance Looking at different ways to address class imbalance in this datasetJohnowhitaker· Over 2 years ago -
A starter kit for R! +Analysis, Features, Training xgboost A end-to-end working notebook for R, with light Exploratory Analysis and Feature Engineeringmichael_bordeleau· Over 2 years ago -
FastAI Tabular Starter Minimal submission making predictions using fastai's tabular learnerJohnowhitaker· Over 2 years ago -
Grid search + voting classifier perform a GS over a voting classifier made of RF and BGsany· Over 2 years ago -
Simple EDA and Baseline - LB 0.66 (0.616 with a magic) Simple EDA and Baseline - LB 0.66 (0.616 with a magic)moto· Over 2 years ago -
