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metadata
dataset_info:
  features:
    - name: Event_ID
      dtype: timestamp
    - name: Timestamp
      dtype: timestamp
    - name: Vehicle_Type
      dtype: text
    - name: Speed_kmh
      dtype: timestamp
    - name: Latitude
      dtype: text
    - name: Longitude
      dtype: text
    - name: Event_Type
      dtype: text
    - name: Severity
      dtype: text
    - name: Traffic_Density
      dtype: text
  splits:
    - name: default
      num_bytes: 0KB
      num_examples: 5

Flowmatic Cleaned Dataset

Overview

This dataset was cleaned and exported by Flowmatic, an intelligent data preparation platform.

Pipeline Run ID: cmm1r1vow0004bt40plobjuj2 Generated: 2026-02-25T08:07:49.270Z

Dataset Statistics

  • Total Records: 5
  • Total Columns: 9
  • File: cleaned_data.csv

Column Information

Column Type Non-Null Null Sample Values
Event_ID timestamp 5 0 "1", "2", "3"
Timestamp timestamp 5 0 "2024-09-13 13:10:48", "2024-10-19 14:34:09", "2024-11-24 18:52:58"
Vehicle_Type text 5 0 "Bus", "Car", "Bus"
Speed_kmh timestamp 5 0 "72", "4", "36"
Latitude text 5 0 "51.101003", "51.116445", "51.15613"
Longitude text 5 0 "71.417789", "71.396492", "71.361778"
Event_Type text 5 0 "Accident", "Normal", "Sudden De-celeration"
Severity text 5 0 "High", "Low", "Low"
Traffic_Density text 5 0 "91.41", "45.52", "21.95"

Data Quality

This dataset has been processed through Flowmatic's cleaning pipeline:

  • ✅ Duplicates removed
  • ✅ Missing values handled (interpolation/forward-fill)
  • ✅ Outliers processed (winsorization)
  • ✅ Type consistency validated
  • ✅ Records exported

Usage

Load the dataset using Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset('username/dataset_name')
df = dataset['train'].to_pandas()

Or load directly as CSV:

import pandas as pd

df = pd.read_csv('https://huggingface.co/datasets/username/dataset_name/raw/main/cleaned_data.csv')

License

This dataset is released under the CC BY 4.0 license.


Processed with Flowmatic