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• Here, we document our work and explain some details regarding techniques used.
• Step by step installation 🖥️

Open Solutions

link to code	name	CV	LB	link to description
solution 1	chestnut 🌰	?	0.742	LightGBM and basic features
solution 2	seedling 🌱	?	0.747	Sklearn and XGBoost algorithms and groupby features
solution 3	blossom 🌼	0.7840	0.790	LightGBM on selected features
solution 4	tulip 🌷	0.7905	0.801	LightGBM with smarter features
solution 5	sunflower 🌻	0.7950	0.804	LightGBM clean dynamic features
solution 6	four leaf clover 🍀	0.7975	0.806	Stacking by feature diversity and model diversity

What could have worked but we haven't tried it 🤔

• Interest rate feature * *
• Adding PCA, T-SNE, Denoising autoencoders * for feature extraction *
• Build model to fill NaN's
• Dedicate more time for neural networks like RNN, 1-D Convolution for time series *
• Using oof prediction from experiments evaluated by grid/random/bayesian search *

Learned lessons 🎓

• Trust your CV!