Add ApplianceDB sample (5 CSVs + parquet, dataset card, viewer configs) mirrored from Kaggle
44e5adf verified | license: odbl | |
| pretty_name: 'ApplianceDB: Home Appliance Error Codes & Ranked Repairs' | |
| language: | |
| - en | |
| tags: | |
| - appliance-repair | |
| - error-codes | |
| - home-appliances | |
| - manufacturing | |
| - engineering | |
| size_categories: | |
| - n<1K | |
| configs: | |
| - config_name: error_codes | |
| data_files: error_codes.csv | |
| - config_name: repair_procedures | |
| data_files: repair_procedures.csv | |
| - config_name: error_codes_fixes_joined | |
| data_files: error_codes_fixes_joined.csv | |
| - config_name: replacement_parts | |
| data_files: replacement_parts.csv | |
| - config_name: brand_recalls | |
| data_files: brand_recalls.csv | |
|  | |
| # ApplianceDB: Home Appliance Error Codes & Ranked Repairs | |
| Relational database mapping 438 home-appliance error codes across 13 brands and 26 (brand, appliance-type) pairs to 288 ranked repair procedures with DIY difficulty tiers. Every code is identified by its composite (brand, appliance_type, code) key — Samsung washer 5C is a distinct record from Samsung dishwasher 5C. | |
| ## Files / configs | |
| Each table is a separate config for the Datasets viewer: | |
| - `error_codes` — 438 fault codes keyed by composite `(brand, appliance_type, code)`, with meaning, component, cause category, severity and source URL | |
| - `repair_procedures` — 288 ranked repair procedures with DIY difficulty tiers | |
| - `error_codes_fixes_joined` — denormalized error-code ↔ ranked-fix join for quick analysis | |
| - `replacement_parts` — OEM replacement parts linked to procedures | |
| - `brand_recalls` — related brand recall references | |
| ## Quick start | |
| ```python | |
| from datasets import load_dataset | |
| codes = load_dataset("Ichlibitiche/appliancedb-error-codes-repair-database", "error_codes", split="train") | |
| fixes = load_dataset("Ichlibitiche/appliancedb-error-codes-repair-database", "repair_procedures", split="train") | |
| ``` | |
| Or with pandas: | |
| ```python | |
| import pandas as pd | |
| codes = pd.read_csv("hf://datasets/Ichlibitiche/appliancedb-error-codes-repair-database/error_codes.csv") | |
| ``` | |
| ## Coverage | |
| - **13 brands:** Beko, Bosch, Candy, Frigidaire, GE, Hoover, Hotpoint, Indesit, LG, Maytag, Miele, Samsung, Whirlpool | |
| - **5 appliance types:** dishwasher, dryer, oven/range, refrigerator, washer | |
| ## Portal & full documentation | |
| Portal: https://appliancedb-public.pages.dev/ · Docs & data dictionary: https://github.com/ApplianceDB/ApplianceDB-public | |
| ## License | |
| **ODbL-1.0** (Open Database License). Facts re-derived from official manufacturer support listings with per-row source URLs; meanings and repair steps paraphrased into original wording. | |
| ## Provenance & Methodology | |
| Every code fact is re-derived from an official manufacturer support listing (LG, Samsung, GE) with the exact source URL recorded per row; all meanings and repair steps are paraphrased into original wording (no verbatim manufacturer prose). Repair ranks carry an explicit basis (manufacturer_first). Costs are left NULL where no verified price observation exists — never guessed. Each release is built by a deterministic, test-gated pipeline (composite-uniqueness, controlled-vocabulary, rank-contiguity, and >=10-codes-per-pair coverage gates). | |
| Full documentation and data dictionary: https://github.com/ApplianceDB/ApplianceDB-public |