Ppdai_risk_evaluation
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|---|---|---|---|---|---|---|---|---|---|---|
| Feature Engineering And Feature Selection | 798 | 2 years ago | 6 | Jupyter Notebook | ||||||
| A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python. | ||||||||||
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| Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself. | ||||||||||
| Awesome Feature Engineering | 316 | 6 years ago | other | |||||||
| A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning | ||||||||||
| Feature Engineering For Machine Learning | 314 | 5 months ago | other | Jupyter Notebook | ||||||
| Code repository for the online course Feature Engineering for Machine Learning | ||||||||||
| Ppdai_risk_evaluation | 124 | 7 years ago | 1 | Jupyter Notebook | ||||||
| “魔镜杯”风控算法大赛 拍拍贷风控模型，接近冠军分数 | ||||||||||
| Kaggle Predict Future Sales | 7 | 6 years ago | Python | |||||||
| Time Series Prediction for Kaggle - Final project - Predict future sales. Highlights: Mean encoding, Feature engineering, Out of fold training and Model Stacking. | ||||||||||
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