Feature Engineering Process

Feature Engineering isn't a single step - it's an iterative process that usually follows a consistent sequence, repeated as you learn more about what actually improves model performance.

1. Understand the Business Problem

Before touching the data, get clear on what outcome you're trying to predict or explain, since that shapes which features are worth engineering.

2. Explore the Raw Data

Use EDA to understand each column's type, distribution, and relationship with the target variable.

3. Brainstorm Candidate Features

Using domain knowledge, list out possible new features - ratios, combinations, extracted date parts, or aggregated statistics.

4. Create the Features

import pandas as pd

df["ExpensePerPerson"] = df["TotalExpense"] / df["FamilySize"]
df["YearsSinceJoining"] = 2026 - df["JoinYear"]

5. Evaluate Feature Usefulness

Check whether the new feature has a meaningful relationship with the target - using correlation, visualisations, or by testing model performance with and without it.

6. Refine and Repeat

Drop features that don't help, keep the ones that do, and continue iterating as the model or business understanding evolves.

A practical rule of thumb: if you can't explain in one sentence why a feature should help predict the target, it's worth testing carefully before trusting it in a model.

Coming Up Next

Next, you'll look at the concrete benefits that good feature engineering brings to a Data Science project.