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🦀 ClawHub

Data Cleaning & Annotation Workflow

by @deyashmukh

Complete workflow for time series datasets (Energy, Manufacturing, Climate) on Kaggle to Data Annotation platform (data.smlcrm.com). Includes downloading, cl...

Versionv1.0.0

Downloads1,293

Installs1

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TERMINAL

clawhub install data-cleaning-annotation-workflow

📖 About This Skill

name: data-cleaning-annotation-workflow description: "Complete workflow for time series datasets (Energy, Manufacturing, Climate) on Kaggle to Data Annotation platform (data.smlcrm.com). Includes downloading, cleaning with pandas, uploading RAW with metadata, configuring columns (Time/Target/Covariate/Group), setting units (kWh, kVarh, tCO2, ratio, seconds), and assigning groups by selecting all variables and applying all group tags. Use when finding Kaggle datasets, cleaning for ML, uploading with metadata, configuring types/units, assigning groups to all variables, or complete pipeline to CLEAN status."

Simulacrum Data Annotation Workflow

Complete end-to-end workflow for time series dataset preparation and annotation on the Data Annotation platform (data.smlcrm.com).

What This Skill Does

This skill captures the precise workflow for processing time series datasets (Energy, Manufacturing, Climate) from discovery to CLEAN status:

1. Find Dataset: Search Kaggle for Energy/Manufacturing/Climate time series data 2. Download: Get CSV files via browser or Kaggle CLI 3. Clean: Run Python/pandas script to handle missing values, duplicates, formatting 4. Upload RAW: Upload original CSV with metadata (name, domain, source URL, description) 5. Configure Headers: Set column types (Time, Target, Covariate, Group) and units 6. Assign Groups: Select ALL variables (target + covariates), apply ALL group tags 7. Upload Cleaned: Final upload → CLEAN status

Supported Domains

Energy: Power consumption, utilities, renewable energy, grid data

Manufacturing: Industrial processes, steel production, emissions, equipment data

Climate: CO2 emissions, environmental monitoring, weather correlation data

Quick Start

For the full pipeline from Kaggle to annotated dataset:

1. Find dataset on Kaggle
2. Download (browser or kaggle CLI)
3. Clean with scripts/clean_dataset.py
4. Upload RAW dataset to data.smlcrm.com (with metadata)
5. Click "Clean" and upload cleaned file
6. Configure column metadata (types, units)
7. Assign groups to variables
8. Upload cleaned dataset → CLEAN status

Workflow Steps

Step 1: Find and Download Dataset

From Kaggle (Browser Method): 1. Navigate to kaggle.com/datasets 2. Search for relevant dataset (e.g., "steel industry energy consumption", "manufacturing emissions", "climate CO2") 3. Review data description, file list, and preview 4. Click "Download" button 5. Extract CSV file from downloaded zip

Alternative: Kaggle CLI

# Install if needed: pip install kaggle
Configure: kaggle competitions list
scripts/download_kaggle.sh  [output-dir]
Example: scripts/download_kaggle.sh csafrit2/steel-industry-energy-consumption

Step 2: Clean the Dataset

Always run the cleaning script before upload:

python3 scripts/clean_dataset.py  [-o ]

What the script does:

Strips whitespace from column names

Removes duplicate rows

Fills missing numeric values with median

Fills missing categorical values with mode or 'Unknown'

Converts timestamp columns to datetime format

Outputs column summary for metadata configuration

Output:

Cleaned CSV file ready for upload

Column summary printed to console (save this for metadata config)

Step 3: Upload Raw Dataset to Platform

1. Navigate to data.smlcrm.com/dashboard 2. Click "Upload Dataset" button 3. Fill in metadata for the RAW dataset: - Name: Descriptive dataset name - Domain: Category (Energy, Manufacturing, Climate, etc.) - Source URL: Kaggle or original source URL - Description: Brief summary of the dataset 4. Upload the original/raw CSV file (not cleaned yet) 5. Click Upload

Result: Dataset appears in list with RAW status

Step 4: Upload Cleaned File & Configure Metadata

1. Find the RAW dataset in the list 2. Click "Clean" button 3. Upload the cleaned CSV file (from Step 2) 4. Configure headers for each column:

| Setting | Description | |---------|-------------| | Name | Column name (editable) | | Units | Measurement units (kWh, °C, %, ratio, tCO2, etc.) | | Type | Time / Target / Covariate / Group |

Column Type Guide:

Time: Timestamp/datetime columns (usually required)

Target: Variable to predict (at least one required)

Covariate: Input features/independent variables

Group: Categorical segment variables (WeekStatus, Day_of_week, Load_Type, etc.)

Bulk Configuration:

Select multiple rows via checkboxes

Use "Apply" dropdown to set type for selected columns

Set units individually or in bulk

Common Unit Patterns:

Energy: kWh, MWh, MW

Power: kVarh, kW

Emissions: tCO2, kgCO2

Ratios: ratio, %

Time: seconds, minutes, hours

Step 5: Assign Groups to Variables

Purpose: Group variables define how data is segmented for analysis.

Exact Workflow: 1. Select ALL variables by checking their checkboxes: - Target variable(s) - ALL covariate variables 2. Apply ALL group tags to selected variables: - Click first group tag (e.g., WeekStatus) → all selected get this group - Click second group tag (e.g., Day_of_week) → all selected get this group - Click third group tag (e.g., Load_Type) → all selected get this group - Continue for all available group tags

3. Result: All variables have all groups assigned (e.g., "WeekStatus × Day_of_week × Load_Type")

Important: Assign groups to BOTH target variables AND all covariates.

Step 6: Final Upload

1. Click "Upload Cleaned Dataset" button 2. Wait for processing 3. Dataset status changes from RAW → CLEAN 4. Verify data points count is correct

Example: Steel Industry Energy Dataset

Source: https://www.kaggle.com/datasets/csafrit2/steel-industry-energy-consumption

Metadata:

Name: Steel Industry Energy Consumption (South Korea)

Domain: Energy

Data Points: 350,400

Column Configuration: | Column | Type | Units | |--------|------|-------| | Timestamps | Time | - | | Usage_kWh | Target | kWh | | Lagging_Current_Reactive.Power_kVarh | Covariate | kVarh | | Leading_Current_Reactive_Power_kVarh | Covariate | kVarh | | CO2(tCO2) | Covariate | tCO2 | | Lagging_Current_Power_Factor | Covariate | ratio | | Leading_Current_Power_Factor | Covariate | ratio | | NSM | Covariate | seconds | | WeekStatus | Group | - | | Day_of_week | Group | - | | Load_Type | Group | - |

Group Assignment: 1. Select: Usage_kWh, Lagging_Current_Reactive.Power_kVarh, Leading_Current_Reactive_Power_kVarh, CO2(tCO2), Lagging_Current_Power_Factor, Leading_Current_Power_Factor, NSM 2. Click: WeekStatus → all selected get WeekStatus 3. Click: Day_of_week → all selected get Day_of_week 4. Click: Load_Type → all selected get Load_Type 5. Final: All variables show "WeekStatus × Day_of_week × Load_Type"

Reference Materials

For detailed platform configuration guidance, see references/platform_guide.md.

Troubleshooting

"Next" button disabled:

Check at least one Time column is set

Check at least one Target column is set

Verify all columns have types assigned

Groups not appearing:

Columns must be marked as "Group" type first

Proceed to next step after setting Group types

Upload fails:

Re-run cleaning script

Check CSV format (comma-delimited)

Verify no empty column names

Scripts

| Script | Purpose | |--------|---------| | scripts/clean_dataset.py | Clean and prepare CSV for upload | | scripts/download_kaggle.sh | Download datasets via Kaggle CLI |

Platform URL

Data Annotation Platform: https://data.smlcrm.com

💡 Examples