What Is Feature Engineering
Feature engineering is the process of creating inputs that help a model learn patterns. Raw data often needs transformation. Strong features improve accuracy and stability.
Why feature engineering is important
- It improves model performance.
- It reduces noise in the dataset.
- It highlights useful patterns.
- It prepares data for algorithms that expect structured inputs.
Main feature engineering actions
- Create new columns from existing ones.
- Encode text or categorical data.
- Scale numerical values.
- Normalize distributions.
- Extract time features like hour or weekday.
- Group rare categories.
Simple Python example
import pandas as pd
df = pd.read_csv("data.csv")
# Create a new feature
df["bmi"] = df["weight"] / (df["height"] ** 2)
# Encode category
df = pd.get_dummies(df, columns=["city"])
# Scale a value
df["age_scaled"] = (df["age"] - df["age"].mean()) / df["age"].std()
Tips for strong features
- Use simple transformations.
- Check correlation between new features and the target.
- Remove features that bring no value.
- Keep track of each transformation.
- Test features with a baseline model.
Conclusion
Feature engineering creates clear inputs for machine learning tasks. It supports AI students and practitioners in reaching stable results. It forms a core skill in every project.
Feature Engineering b Darija
Feature engineering huwa lprocess li katsayeb inputs jdadin bach lmodel ytfham data mzyan. Data raw kats7taj tbdil. Features mqaddmin kayrfa3o performance dyal model.
Ash bhal faida dyalo
- Kayhssen accuracy.
- Kayn9i noise.
- Kaybayyen patterns mhemmin.
- Kayywajjid data l algorithms.
Steps m3roufin f feature engineering
- Tsayeb columns jdadin.
- Encode text w categories.
- Scale values numeriques.
- Normalize distributions.
- Tsayeb time features bhal hour w weekday.
- Tjam3 categories nqallin.
Exemple b Python
import pandas as pd
df = pd.read_csv("data.csv")
df["bmi"] = df["weight"] / (df["height"] ** 2)
df = pd.get_dummies(df, columns=["city"])
df["age_scaled"] = (df["age"] - df["age"].mean()) / df["age"].std()
Tips
- Khdem b steps s7la.
- Chouf correlation m3a target.
- Hayed features li mafihom faida.
- Sjjel kull bdl.
- Testi features b model basique.
Khitam
Feature engineering kaywajjid inputs wadiin. Kay3awn talaba w practitioners f AI. Kayb9a skill mhemma f kol project.