: A classic resource for academic and professional datasets.

: Use One-Hot Encoding for nominal data (e.g., "State") or Label Encoding for ordinal data.

: Handle missing values by using imputation (mean/median) or dropping incomplete rows.

If you are working on a legitimate data science project and need to practice feature engineering, I recommend using verified, public datasets. Here are a few safe alternatives:

: Create new variables, such as calculating "Years of Credit History" from "Account Open Date."

: Provides extensive, anonymized USA demographic data for feature engineering. How to Prepare Features for a Standard Dataset

If you transition to a legitimate dataset, here is the standard workflow for preparing features: