: 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 transition to a legitimate dataset, here is the standard workflow for preparing features: 900k_USA_dump.txt
: Provides extensive, anonymized USA demographic data for feature engineering. How to Prepare Features for a Standard Dataset
: Use StandardScaler or MinMaxScaler to ensure numerical features (like "Income" or "Age") are on a similar scale. : Use One-Hot Encoding for nominal data (e
: Create new variables, such as calculating "Years of Credit History" from "Account Open Date."
: Offers thousands of structured datasets (CSV, JSON) for tasks like credit scoring, housing prices, or demographic analysis. How to Prepare Features for a Standard Dataset
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: