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khalidml65
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lottery-draw-machine-telemetry

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Tags:
tabular
lottery
predictive-maintenance
anomaly-detection
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
game_id: int64
draw_id: string
machine_id: string
ball_set_id: string
venue_id: double
source_record_id: int64
draw_local_datetime: string
timezone: int64
draw_datetime_utc: int64
city: int64
country: int64
latitude: double
longitude: double
temperature_c: double
main_numbers: int64
bonus_numbers: int64
weather_observed_at: int64
weather_match_difference: double
record_status: int64
source_confidence: int64
retrieved_at: int64
schema_version: int64
time_basis: int64
location_confidence: int64
draw_count_since_install: double
cumulative_usage: double
time_since_last_service: double
recent_machine_changes: double
latitude_rolling_mean: double
latitude_rolling_var: double
latitude_rolling_sum: double
latitude_rolling_range: double
latitude_rolling_freq_dev: double
longitude_rolling_mean: double
longitude_rolling_var: double
longitude_rolling_sum: double
longitude_rolling_range: double
longitude_rolling_freq_dev: double
temperature_c_rolling_mean: double
temperature_c_rolling_var: double
temperature_c_rolling_sum: double
temperature_c_rolling_range: double
temperature_c_rolling_freq_dev: double
weather_match_difference_rolling_mean: double
weather_match_difference_rolling_var: double
weather_match_difference_rolling_sum: double
weather_match_difference_rolling_range: double
weather_match_difference_rolling_freq_dev: double
draw_count_since_install_rolling_mean: double
draw_count_since_install_rolling_var: double
draw_count_since_install_rolling_sum: double
draw_count_since_instal
...
st_service_rolling_range_rolling_zscore: double
time_since_last_service_rolling_range_drift: double
time_since_last_service_rolling_range_recent_anomalies: double
time_since_last_service_rolling_freq_dev_rolling_zscore: double
time_since_last_service_rolling_freq_dev_drift: double
time_since_last_service_rolling_freq_dev_recent_anomalies: double
recent_machine_changes_rolling_mean_rolling_zscore: double
recent_machine_changes_rolling_mean_drift: double
recent_machine_changes_rolling_mean_recent_anomalies: double
recent_machine_changes_rolling_var_rolling_zscore: double
recent_machine_changes_rolling_var_drift: double
recent_machine_changes_rolling_var_recent_anomalies: double
recent_machine_changes_rolling_sum_rolling_zscore: double
recent_machine_changes_rolling_sum_drift: double
recent_machine_changes_rolling_sum_recent_anomalies: double
recent_machine_changes_rolling_range_rolling_zscore: double
recent_machine_changes_rolling_range_drift: double
recent_machine_changes_rolling_range_recent_anomalies: double
recent_machine_changes_rolling_freq_dev_rolling_zscore: double
recent_machine_changes_rolling_freq_dev_drift: double
recent_machine_changes_rolling_freq_dev_recent_anomalies: double
hour_of_day: double
day_of_week: double
month: double
season: int64
temperature_rolling_mean: double
missingness_ratio: double
missingness_flag: double
anomaly_score: null
is_anomaly: null
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 37667
to
{'draw_id': Value('int64'), 'game_id': Value('int64'), 'machine_id': Value('string'), 'venue_id': Value('string'), 'draw_local_datetime': Value('string'), 'temperature_c': Value('float64'), 'anomaly_score': Value('float64'), 'is_anomaly': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2406, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2280, in cast_table_to_features
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              game_id: int64
              draw_id: string
              machine_id: string
              ball_set_id: string
              venue_id: double
              source_record_id: int64
              draw_local_datetime: string
              timezone: int64
              draw_datetime_utc: int64
              city: int64
              country: int64
              latitude: double
              longitude: double
              temperature_c: double
              main_numbers: int64
              bonus_numbers: int64
              weather_observed_at: int64
              weather_match_difference: double
              record_status: int64
              source_confidence: int64
              retrieved_at: int64
              schema_version: int64
              time_basis: int64
              location_confidence: int64
              draw_count_since_install: double
              cumulative_usage: double
              time_since_last_service: double
              recent_machine_changes: double
              latitude_rolling_mean: double
              latitude_rolling_var: double
              latitude_rolling_sum: double
              latitude_rolling_range: double
              latitude_rolling_freq_dev: double
              longitude_rolling_mean: double
              longitude_rolling_var: double
              longitude_rolling_sum: double
              longitude_rolling_range: double
              longitude_rolling_freq_dev: double
              temperature_c_rolling_mean: double
              temperature_c_rolling_var: double
              temperature_c_rolling_sum: double
              temperature_c_rolling_range: double
              temperature_c_rolling_freq_dev: double
              weather_match_difference_rolling_mean: double
              weather_match_difference_rolling_var: double
              weather_match_difference_rolling_sum: double
              weather_match_difference_rolling_range: double
              weather_match_difference_rolling_freq_dev: double
              draw_count_since_install_rolling_mean: double
              draw_count_since_install_rolling_var: double
              draw_count_since_install_rolling_sum: double
              draw_count_since_instal
              ...
              st_service_rolling_range_rolling_zscore: double
              time_since_last_service_rolling_range_drift: double
              time_since_last_service_rolling_range_recent_anomalies: double
              time_since_last_service_rolling_freq_dev_rolling_zscore: double
              time_since_last_service_rolling_freq_dev_drift: double
              time_since_last_service_rolling_freq_dev_recent_anomalies: double
              recent_machine_changes_rolling_mean_rolling_zscore: double
              recent_machine_changes_rolling_mean_drift: double
              recent_machine_changes_rolling_mean_recent_anomalies: double
              recent_machine_changes_rolling_var_rolling_zscore: double
              recent_machine_changes_rolling_var_drift: double
              recent_machine_changes_rolling_var_recent_anomalies: double
              recent_machine_changes_rolling_sum_rolling_zscore: double
              recent_machine_changes_rolling_sum_drift: double
              recent_machine_changes_rolling_sum_recent_anomalies: double
              recent_machine_changes_rolling_range_rolling_zscore: double
              recent_machine_changes_rolling_range_drift: double
              recent_machine_changes_rolling_range_recent_anomalies: double
              recent_machine_changes_rolling_freq_dev_rolling_zscore: double
              recent_machine_changes_rolling_freq_dev_drift: double
              recent_machine_changes_rolling_freq_dev_recent_anomalies: double
              hour_of_day: double
              day_of_week: double
              month: double
              season: int64
              temperature_rolling_mean: double
              missingness_ratio: double
              missingness_flag: double
              anomaly_score: null
              is_anomaly: null
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 37667
              to
              {'draw_id': Value('int64'), 'game_id': Value('int64'), 'machine_id': Value('string'), 'venue_id': Value('string'), 'draw_local_datetime': Value('string'), 'temperature_c': Value('float64'), 'anomaly_score': Value('float64'), 'is_anomaly': Value('int64')}
              because column names don't match

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Dataset Card — LD ML 03: Lottery Machine Operational Data

Dataset ID: ld-ml-03-lottery-machine-ops
Version: 0.1.0
Last Updated: 2026-09-14
Task Category: Anomaly Detection (Unsupervised)

⚠️ DISCLAIMER
This dataset and derived models do not support prediction of winning lottery numbers. Draw outcome columns (ball numbers, positions) are excluded from all feature engineering and model training.

1. Dataset Summary

This dataset contains historical operational records from lottery draw machines, capturing mechanical, environmental, and scheduling metadata for each draw event. It is used exclusively for unsupervised machine anomaly detection — identifying unusual operating patterns that may warrant maintenance inspection.

No player data, ticket information, or draw outcomes (winning numbers) are included or processed.

2. Schema / Data Dictionary

Column Name Type Description
game_id integer Unique identifier for the lottery game type
draw_id integer Unique identifier for an individual draw event
machine_id string Identifier for the physical lottery draw machine
ball_set_id string Identifier for the set of balls used in the draw
venue_id string Identifier for the draw venue or studio
draw_local_datetime datetime (UTC) Timestamp of the draw in local time, stored as UTC
temperature_c float Ambient temperature at the venue in degrees Celsius

Note: Additional operational columns may be present in source data but are not guaranteed to be populated across all records.

3. Source and Provenance

  • Data owner: Lottery operations authority (internal).
  • Collection method: Automated draw management system logs, exported to CSV format.
  • Geographic scope: Single jurisdiction (details withheld for data governance reasons).
  • Time range: Historical records spanning multiple years; exact range TBD pending data delivery.

4. Processing Steps

  1. Loading: CSV ingestion via src/data/loader.py with schema validation.
  2. Datetime parsing: draw_local_datetime parsed to UTC-aware Timestamp.
  3. Chronological sorting: Records sorted by draw_local_datetime before any processing.
  4. Missing value imputation: Numeric columns imputed with column median; categorical columns filled with "__missing__" placeholder.
  5. Encoding: Categorical features label-encoded; numeric features standard-scaled (fit on training split only).
  6. Feature engineering: Machine history, rolling distribution diagnostics, temporal stability, and environmental features added (all leakage-safe).

5. Splits

Split Date Boundary Purpose
Training ≤ 2023-06-30 Model fitting
Validation 2023-07-01 – 2023-12-31 Hyperparameter selection, threshold tuning
Test ≥ 2024-01-01 Final evaluation

Splits are strictly chronological (no shuffling) to prevent temporal leakage.

6. License Notes

  • The dataset is proprietary to the lottery operations authority.
  • It may not be redistributed, published, or used for purposes other than internal operational analytics without written approval.
  • This repository does not contain or commit the raw data; only the processing and modelling code is version-controlled.

7. Known Gaps

Gap Impact Mitigation
No verified machine failure / maintenance event labels Precludes supervised classification Unsupervised anomaly detection used instead
Incomplete temperature records in some periods Rolling temperature features may be NaN Median imputation applied; missingness flag added
Unknown ball set retirement / replacement history Service interval features may be inaccurate time_since_last_service flagged with NaN for first draw per machine
Single jurisdiction data Model may not generalise to other lotteries Document scope limitation; re-train for new jurisdictions
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