Rewrite dataset card: full column docs, fault census, decision math

README.md

@@ -7,42 +7,121 @@ tags:
 - medallion-architecture
 - databricks
 - data-quality
+- olap
 size_categories:
 - 100K<n<1M
+configs:
+- config_name: messy_stream_2024
+  data_files: 1_car_telemetry_2024.csv
+- config_name: charging_sessions
+  data_files: 2_charging_sessions.csv
+- config_name: grid_carbon
+  data_files: 3_grid_carbon_hourly.csv
+- config_name: archive_rare_user
+  data_files: archive_2020_2024_rare_user.csv
+- config_name: archive_daily_user
+  data_files: archive_2020_2024_daily_user.csv
+- config_name: archive_moderate_user
+  data_files: archive_2020_2024_moderate_user.csv
+- config_name: archive_heavy_user
+  data_files: archive_2020_2024_heavy_user.csv
 ---
 
-# EV Fleet Sensor Data — Medallion Case Study
+# ⚡ EV Fleet Sensor Data — Databricks Medallion Case Study
 
-Raw source CSVs for a **Databricks medallion architecture** case study:
-from a messy EV sensor stream to decision-grade insights (battery health, DTC risk,
-charger siting, clean-charging windows).
+Raw source CSVs for an end-to-end **Databricks medallion architecture** (Bronze → Silver → Gold) case study:
+a deliberately messy EV sensor stream is cleaned, quality-gated, and distilled into decision-grade insights —
+battery health ranking, DTC risk, charger siting, and a clean-charging window.
 
-## The three feeds (three owners)
+- **Question data:** `1_car_telemetry_2024.csv` — 35,363 rows of 2024 telemetry with **planted faults**
+- **Answer key:** the four `archive_2020_2024_*.csv` files — a clean ~175K-row sensor archive (2020–2024)
+- The messy 2024 stream was **derived from the archive** (faults planted, seed = 7), so every fault is verifiable.
+  The two sources intentionally overlap in 2024 (stream = question, archive = ground truth).
 
-| File | What it is | Rows | Role |
-|---|---|---|---|
-| `1_car_telemetry_2024.csv` | Hourly EV telemetry for 2024 — SOC, SOH, temps, torque, tire pressure, DTC codes | ~35K | **Messy stream** (planted faults: duplicates, kPa-vs-PSI units, out-of-bounds, missing) |
-| `2_charging_sessions.csv` | Charging sessions: district, duration, energy | 484 | Charger-siting question |
-| `3_grid_carbon_hourly.csv` | Hourly grid carbon intensity (gCO₂/kWh) | 168 | Clean-charging-window question |
+## Dataset Structure
 
-## The clean archive (answer key)
+### 🔴 Feed 1 — Car telemetry (the messy stream)
 
-| File | Vehicle profile | Rows |
+**`1_car_telemetry_2024.csv`** — hourly readings, 4 vehicles, calendar year 2024.
+
+| Column | Type | Description |
 |---|---|---|
-| `archive_2020_2024_rare_user.csv` | Renault Zoe · rare user | ~43K |
-| `archive_2020_2024_daily_user.csv` | Hyundai Ioniq 5 · daily user | ~44K |
-| `archive_2020_2024_moderate_user.csv` | VW ID.4 · moderate user | ~44K |
-| `archive_2020_2024_heavy_user.csv` | Ford F-150 Lightning · heavy user | ~44K |
+| `timestamp` | datetime | Hour of reading |
+| `vehicle_id` | string | `EV-101` … `EV-104` |
+| `user_profile` | string | `rare_user` / `daily_user` / `moderate_user` / `heavy_user` |
+| `district` | string | Home district: University, Downtown, Harbor, Industrial |
+| `SOC` | float | State of charge, % |
+| `SOH` | float | State of health (battery), % |
+| `Charging_Cycles` | int | Lifetime charge cycles |
+| `charging_now` | int | 0/1 — currently charging |
+| `Battery_Temp` | float | Battery temperature, °C |
+| `Motor_RPM` | int | Motor speed |
+| `Motor_Torque` | float | Motor torque, Nm |
+| `Motor_Temp` | float | Motor temperature, °C |
+| `Brake_Pad_Wear` | float | Brake pad wear, % |
+| `Charging_Voltage` | float | Charging voltage, V |
+| `Tire_Pressure` | float | Tire pressure — ⚠️ planted unit faults (kPa vs PSI) |
+| `DTC` | int | 1 = diagnostic trouble code active |
+| `grid_carbon_g_per_kwh` | float | Grid carbon intensity at that hour |
+
+**Planted fault census:** exact duplicates (419, to be dropped), kPa→PSI unit errors (fix),
+out-of-bounds values (fix + flag), missing readings (flag). Silver-layer policy:
+*only duplicates die — the row always survives; the faulty value is nulled.*
+
+### 🔌 Feed 2 — Charging sessions
+
+**`2_charging_sessions.csv`** — 484 completed sessions.
+
+| Column | Description |
+|---|---|
+| `session_id`, `station_id` | Identifiers |
+| `district` | Where the session happened (drives charger-siting analysis) |
+| `start_time`, `end_time` | Session window |
+| `energy_kwh_source_analog`, `power_kw_analog` | Energy delivered / power draw |
+| `soc_start_pct`, `soc_end_pct` | Battery % before → after |
+| `battery_kwh`, `vehicle_model` | Vehicle pack size and model |
+| `station_temp_c` | Station temperature |
+| `status` | Session status |
+
+### ⚡ Feed 3 — Grid carbon intensity
 
-Clean 2020–2024 hourly sensor archive (~175K rows total). The 2024 messy stream is
-**derived from this archive** (faults planted with seed 7) — archive = ground truth,
-stream = the question. They intentionally overlap in 2024 so every fault is verifiable.
+**`3_grid_carbon_hourly.csv`** — 168 rows (7 days × 24 h): `date`, `hour`, `carbon_g_per_kwh`.
+Midnight peaks near ~911 gCO₂/kWh; midday (~12:00) drops to ~319.
+
+### 🟢 The clean archive (ground truth)
+
+Four files, one per user profile, each with **43,800 rows** (24 h × 365 d × 5 yrs, 2020–2024):
+
+`archive_2020_2024_{rare,daily,moderate,heavy}_user.csv` — same sensor columns as the stream
+(leading index column, no timestamps/district).
+
+| File | Vehicle | Profile |
+|---|---|---|
+| `archive_2020_2024_rare_user.csv` | Renault Zoe (52 kWh) | EV-101 |
+| `archive_2020_2024_daily_user.csv` | Hyundai Ioniq 5 (77 kWh) | EV-103 |
+| `archive_2020_2024_moderate_user.csv` | VW ID.4 (82 kWh) | EV-102 |
+| `archive_2020_2024_heavy_user.csv` | Ford F-150 Lightning (131 kWh) | EV-104 |
 
 ## Example decisions this data supports
 
-- **Risk score** = `soh_decay_pct_per_year × 20 + dtc_rate_pct × 10` (thresholds 60/30)
-- Service EV-102 first (risk 54.1); site the next charger in the Industrial district
-  (43,311 gCO₂/vehicle-day, 86 sessions); shift charging to ~12:00 (~319 vs ~911 gCO₂/kWh).
+Transparent, verifiable arithmetic (no black-box model):
+
+1. **Risk score** = `soh_decay_pct_per_year × 20 + dtc_rate_pct × 10`, thresholds 60 (act) / 30 (watch)
+   → EV-102 scores **54.1** (decay 1.958 %/yr + 654 DTC events) → service first
+2. **Charger siting** = `total_co2_g ÷ vehicle_days`
+   → Industrial district worst at **43,311 gCO₂/vehicle-day** across 86 sessions → site the next charger there
+3. **Charging window** = cleanest vs dirtiest hour
+   → shift charging to **~12:00**: ~319 vs ~911 gCO₂/kWh (2.86× cleaner, ~592 g saved per kWh shifted)
+
+## How it was used
+
+The files are ingested as-is into a Databricks Free Edition lakehouse:
+
+```
+Bronze (raw, immutable)  →  Silver (cleaned, quality gate: 35,363 − 419 dupes = 34,944 rows)
+                         →  Gold (6 decision tables: soh_trend, dtc_risk_rank, co2_per_vehicle_day,
+                                  grid_carbon_by_district_hour, fleet_worry_list, final_decision)
+```
 
 ## Usage
 
@@ -55,3 +134,13 @@ path = hf_hub_download(repo_id="jeyanthiii/sample_ev_data_csv",
                        repo_type="dataset")
 df = pd.read_csv(path)
 ```
+
+## Notes
+
+- Faults are **synthetic and intentional** (teaching/demo dataset); the archive is the reference truth.
+- 2024 rows exist in both stream and archive **by design** — that overlap is what makes the quality gate provable.
+- Local backup of this folder is kept at `~/Documents/work/temp/cl_demo/data/`.
+
+## License
+
+[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) — free to use with attribution.