# ashishpatel26/Amazing-Feature-Engineering

<https://repo.masters-masters.com/repos/ashishpatel26/Amazing-Feature-Engineering>

[Accueil](https://repo.masters-masters.com/) › [ashishpatel26](https://repo.masters-masters.com/owners/ashishpatel26) › [Amazing-Feature-Engineering](https://repo.masters-masters.com/repos/ashishpatel26/Amazing-Feature-Engineering)

ashishpatel26/Amazing-Feature-Engineering, dépôt open source à 807 étoiles sur GitHub. Carte Bêta de RepoMasters. Feature engineering is the process of using…

| Champ | Valeur |
| --- | --- |
| Propriétaire | [ashishpatel26](https://repo.masters-masters.com/owners/ashishpatel26) |
| Étoiles | 807 |
| Forks | 273 |
| Langage | [Jupyter Notebook](https://repo.masters-masters.com/languages/jupyter-notebook) |
| Licence | Aucune |
| Création | 2019 |
| Sujets | feature-engineering, machine-learning, deep-learning, scikit-learn, data-mining, data-science, data-scientists, feature-scaling, feature-extraction, feature-selection |
| Rang | [Bêta (700 à 1 999 ★)](https://repo.masters-masters.com/ranks/box_office) |
| Voir sur GitHub | [ashishpatel26/Amazing-Feature-Engineering](https://github.com/ashishpatel26/Amazing-Feature-Engineering) |

## Fiche du dépôt

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.

Rang des dépôts qui ont 700 à 1 999 ★ sur GitHub. Probabilité publiée par carte tirée : 6 %. Rareté : Rare.

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