Data Cleaning

• Rename columns for clarity and drop any columns that won’t be useful for analysis.

• Format/convert data: e.g., ensure numbers are integers/floats and dates are in a consistent format.

• Handle missing data: fill in, impute, or drop missing values if they’re below a chosen threshold (e.g., less than 3% missing).

• Check for duplicated rows or inconsistent formats and fix them as needed.