Feature engineering is the process of selecting, transforming, and extracting relevant features or variables from raw data to create a set of input features that can be used to build a predictive model. It is a critical step in the data science pipeline that involves understanding the domain of the problem, selecting the appropriate features, and transforming them to improve the performance of the model. The process of feature engineering can involve several techniques such as:

• Imputation of missing values: When data contains missing values, various imputation techniques can be used to fill in the missing values.
• Detecting outliers: Outliers negatively affect the distribution of data and should be avoided with various techniques.
• Encoding categorical variables: Categorical variables need to be encoded into numerical values to be used in machine learning models.
• Scaling and normalization: Features can be scaled or normalized to ensure that they have similar ranges and magnitudes.
• Feature selection: Not all features may be relevant for a particular problem, and some features may even introduce noise into the model. Feature selection techniques can help identify the most important features for the model.
• Feature extraction: Sometimes, the raw data may not contain relevant features for the problem. In such cases, feature extraction techniques can be used to derive new features from the raw data.