Feature Engineering: The Ultimate Guide

Feature engineering is the process of selecting, manipulating, and transforming raw data into features that can be used in both supervised and unsupervised learning. It's essentially the art of turning raw data into meaningful information that a machine-learning model can understand and utilize effectively.

Reasons for feature engineering
The quality of your features plays a crucial role in determining the effectiveness of your machine-learning-model. High-quality features can:

Boost model accuracy: By effectively capturing essential information and minimizing noise.
Improve interpretability: By ensuring the features are meaningful and easy to understand.
Accelerate training: By reducing data dimensionality, which can speed up the training process.

Typical Steps in Feature Engineering

1. Feature Creation-creation of new features from existing data to help with better predictions (encoding, binning).
2. Feature Transformations-transformation of data to improve the accuracy of the algorithm.
3. Feature Extraction- transforming raw data into the desired form.
4. Feature Selection- choosing relevant features for your problem and removing unnecessary features. Three techniques used include filter-based, wrapper-based, and embedded approaches. The process consists of four basic steps namely, subset generation, subset evaluation, stopping criterion, and result validation.

Techniques for Feature Engineering

1. Imputation: Handling Missing Data. This method replaces the missing values in the dataset with a statistic such as mean, median or mode.
2. One-Hot Encoding: Encoding Categorical Variables. Converting categorical features into numerical representations.
3. Polynomial Features: Creating new features by combining existing ones.
4. Feature Scaling: Normalize features to a common scale to improve model performance.
5. Interaction Features: Creating features that capture the interaction between two or more features.
6. Normalization: Scaling features to a specific range (e.g., 0-1).

Examples of feature engineering use cases.

Tracking how often teachers assign different grades.
Calculating a person's age based on their birth date and the current date.
Counting words and phrases in news articles.
Determining the average and median retweet count for specific tweets.

In conclusion, feature engineering requires technical knowledge about machine learning models, algorithms, coding and data engineering in order to use it effectively.