Feature engineering can be defined as the process of selecting, extracting and transforming raw data into features that are suitable for machine learning models.

This can be achieved through a number of techniques:

Domain Knowledge: Utilize knowledge from the field to create features that capture important aspects of the data. For example, in financial data, you might create features like moving averages or volatility.
Mathematical Transformations: Apply mathematical operations to existing features, such as taking logarithms or creating polynomial features, to capture non-linear relationships.
Feature Extraction:

Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) can reduce the number of features while retaining essential information. This helps in simplifying models and reducing overfitting.
Text Feature Extraction: For text data, methods like Term Frequency-Inverse Document Frequency (TF-IDF) or embeddings like Word2Vec transform text into numerical features.
Feature Selection:

Filter Methods: Use statistical tests or correlation coefficients to select features that have a strong relationship with the target variable.
Wrapper Methods: Use algorithms to evaluate the performance of feature subsets and select the best combination of features.
Embedded Methods: Use feature selection techniques integrated within the learning algorithm, such as regularization in linear models.
Handling Missing Values:

Imputation: Fill missing values using statistical methods or machine learning models to ensure that the dataset remains complete and useful.
Feature Engineering: Create indicators for missing values or use domain knowledge to handle them appropriately.
Normalization and Scaling:

Standardization: Transform features to have zero mean and unit variance, which helps many algorithms perform better.
Min-Max Scaling: Scale features to a fixed range, typically [0, 1], which is useful for algorithms sensitive to the scale of the input data.