secondmover/ashrae-great-energy-predictor-3-solution-analysis

Analysis of all ML models

ASHRAE - Great Energy Predictor III - Top 5 winning solutions

This repository is for analysing all solutions, both winning and other public kernals, and summarizing their characteristics -- ML workflow, score comparison across different sites, building types, meter types, etc.

Links

1. Top 5 winning solutions - code and docs (submitted to Kaggle)
2. Top 5 winning solutions - explainer videos

First rank solution

• Code
• Solution summary document (.docx)
• Solution summary presentation (.pdf)
• Sulution summay in Kaggle discussion board
• Solution explainer video

Second rank solution

• Code
• Solution summary document (.pdf)
• Solution summary presentation (.pdf)
• Sulution summay in Kaggle discussion board
• Solution explainer video

Third rank solution

• Code
• Solution summary document (.pdf)
• Sulution summay in Kaggle discussion board
• Solution explainer video

Fourth rank solution

• Code
• Solution summary document (.pdf)
• Sulution summay in Kaggle discussion board
• Solution explainer video

Fifth rank solution

• Code
• Solution summary document (.docx)
• Solution summary presentation (.pdf)
• Sulution summay in Kaggle discussion board
• Solution explainer video

Comparison

	Final Rank	Team Name	Final Private Leaderboard Score	Preprocessing Strategy	Features Strategy Overview	Modeling Strategy Overview	Post-Processing strategy
0	1	Isamu & Matt	1.231	Removed anomalies in meter data and imputed missing values in weather data	28 features, Extensively focused on feature engineering and selected	LightGBM, CatBoost, and MLP models trained on different subsets of the training and public data	Ensembled the model predictions using weighted generalized mean.
1	2	cHa0s	1.232	Visual analytics and manual inspection	Raw energy meter data, temporal features, building metadata, simple statistical features of weather data.	XGBoost, LightGBM, Catboost, and Feed-forward Neural Network models trained on different subset of the training set	Weighted mean. (different weights were used for different meter types)
2	3	eagle4	1.234	Eliminated 0s in the same period in the same site	nan	Keras CNN, LightGBM and Catboost	nan
3	4	不用leakage上分太难了	1.235	Not available	23 features including raw data, aggregate, weather lag features, and target encoding. Features are selected using sub-training sets.	XGBoost (2-fold, 5-fold) and Light GBM (3-fold)	Ensembled three models. Weights were determined using the leaked data.
4	5	mma	1.237	Dropped long streaks of constant values and zero target values.	Target encoding using percentile and proportion and used the weather data temporal features	LightGBM in two steps -- identify model parameters on a subset and then train on the whole set for each building.	Weighted average.