900k_usa_dump.txt < Best Pick >

: 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. 900k_USA_dump.txt

If you transition to a legitimate dataset, here is the standard workflow for preparing features: : Use One-Hot Encoding for nominal data (e

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: I recommend using verified