Dataset Cleaning Pass

Audit this CSV/tabular dataset for issues before it's used in analysis — report findings first, don't silently modify the data: - Duplicates: exact duplicate rows, and near-duplicates that likely represent the same entity (e.g. inconsistent casing/spacing in a name/email column). - Missing values: how are they actually encoded — truly blank, "N/A", "null", "-", 0 used as a placeholder? Inconsistent encodings of "missing" across the same column are a common, easy-to-miss problem. - Formatting consistency: dates in more than one format, inconsistent casing in categorical columns, inconsistent units. - Type mismatches: a column that's mostly numeric with a few text values mixed in (often from a typo or a placeholder like "unknown"). - Outliers: values far outside the range of the rest of the column — flag them for review, don't assume they're errors and remove them. Report column by column, with counts ("14 rows have inconsistent date formats in signup_date"), not just a general "data looks messy." Only clean/transform the data after confirming which findings are actually errors versus legitimate values.