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:
: Handle missing values by using imputation (mean/median) or dropping incomplete rows. 900k_USA_dump.txt
: Use StandardScaler or MinMaxScaler to ensure numerical features (like "Income" or "Age") are on a similar scale. If you are working on a legitimate data
: Use One-Hot Encoding for nominal data (e.g., "State") or Label Encoding for ordinal data. I recommend using verified
: Offers thousands of structured datasets (CSV, JSON) for tasks like credit scoring, housing prices, or demographic analysis.