Create README.md

README.md

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+---
+
+license: other
+tags:
+- csv
+- data-cleaning
+- geonames
+- hr-data
+
+configs:
+- config_name: main_data
+  data_files:
+    - split: train
+      path: "cleaned.csv"
+      sep: ","
+      default: true
+- config_name: original_data
+  data_files: "HR_Data_Clean_2020_2025.csv"
+  sep: ","
+
+---
+
+  # Repair Data (Cleaned Preview)
+
+  This dataset publishes the contents of the repair_data folder, with the dataset UI preview focused on cleaned.csv. The cleaned file is produced with
+  csv_repair.py to standardize types, trim whitespace, harmonize null-like tokens, and optionally split a location column into city and country. City names
+  can be made country-specific using a real city catalog built from GeoNames.
+
+  ## Files
+
+  - cleaned.csv — primary data file targeted for preview.
+  - Other helper scripts and reports are included for reproducibility (e.g., csv_repair.py, extract_countries.py, geonames_fetch.py, JSON mappings under
+    report/).
+
+  If cleaned.csv lives in a subfolder, update the front‑matter viewer.default_path to that path (e.g., report/cleaned.csv).
+
+  ## How It Was Built
+
+  - Analysis and cleaning: csv_repair.py (trims strings, standardizes boolean‑like values, parses dates where feasible, detects outliers, suggests column
+    fixes).
+  - Location repair (optional): splits location into city and country.
+      - Real city mode: --location-mode real --cities-json report/cities_by_country.json.
+  - GeoNames data: geonames_fetch.py builds countries.json, provinces_by_country.json, and cities_by_country.json.
+
+  Example command to generate cleaned.csv:
+
+  python csv_repair.py \
+    -i HR_Data_Clean_2020_2025.csv \
+    -o report \
+    --cleaned-csv cleaned.csv \
+    --fix-location \
+    --location-mode real \
+    --cities-json report/cities_by_country.json
+
+  ## Load Examples
+
+  - With datasets:
+
+  from datasets import load_dataset
+  ds = load_dataset("savedata101/repair_data", data_files={"train": "cleaned.csv"})
+  print(ds["train"])
+
+  - With pandas (direct URL to main branch):
+
+  import pandas as pd
+  url = "https://huggingface.co/datasets/savedata101/repair_data/resolve/main/cleaned.csv"
+  df = pd.read_csv(url)
+  print(df.head())
+
+  ## Notes
+
+  - Preview focuses on cleaned.csv via the dataset card front‑matter.
+  - If your cleaned file path changes, keep viewer.default_path in sync.
+  - Large original CSVs may be excluded from preview but remain accessible in the repo.
+
+  ## License
+
+  Data license is set to other as a placeholder. Please update to the appropriate license for your data.