Brand string | Type int64 | Ink Color int64 | Length_mm float64 | Mass_g float64 | Line_Width_mm float64 | Line_Width_Binary int64 |
|---|---|---|---|---|---|---|
Pilot | 0 | 0 | 149.49 | 7 | 0.32 | 0 |
Staedtler | 0 | 4 | 142.4 | 10.44 | 0.55 | 0 |
Bic | 2 | 2 | 145.9 | 14.3 | 0.87 | 1 |
Pilot | 1 | 3 | 140.8 | 12.3 | 0.53 | 0 |
Pilot | 2 | 0 | 147 | 10.23 | 0.86 | 1 |
Pentel | 1 | 0 | 144.94 | 10.7 | 0.49 | 0 |
Pilot | 1 | 1 | 146 | 13 | 0.5 | 0 |
Zebra | 2 | 1 | 152.69 | 10.14 | 0.79 | 1 |
PaperMate | 3 | 2 | 138.5 | 16.94 | 1.23 | 1 |
Sharpie | 0 | 1 | 144 | 5.9 | 0.37 | 0 |
Pentel | 0 | 1 | 144 | 10 | 0.34 | 0 |
Staedtler | 1 | 1 | 146.1 | 11.9 | 0.62 | 1 |
Writech | 2 | 1 | 149.4 | 11 | 0.67 | 1 |
Writech | 0 | 0 | 148.41 | 8 | 0.41 | 0 |
Pilot | 1 | 3 | 143 | 13 | 0.47 | 0 |
Writech | 0 | 0 | 148 | 9.32 | 0.29 | 0 |
Pilot | 1 | 1 | 152.3 | 10 | 0.5 | 0 |
Pentel | 1 | 0 | 139.8 | 10.6 | 0.55 | 0 |
Pilot | 3 | 1 | 141 | 19.5 | 1.42 | 1 |
Bic | 1 | 1 | 147 | 15 | 0.52 | 0 |
PaperMate | 0 | 1 | 144.7 | 11 | 0.23 | 0 |
Staedtler | 2 | 0 | 130 | 11.4 | 0.95 | 1 |
Bic | 0 | 1 | 152 | 9.15 | 0.5 | 0 |
PaperMate | 1 | 1 | 150.7 | 10 | 0.6 | 0 |
Uniball | 1 | 0 | 149.3 | 10.97 | 0.43 | 0 |
Uniball | 1 | 0 | 144.7 | 16.8 | 0.44 | 0 |
Uniball | 3 | 1 | 144.8 | 15.3 | 1.12 | 1 |
Pilot | 1 | 2 | 149 | 13.4 | 0.51 | 0 |
Crayola | 0 | 1 | 152.2 | 6.69 | 0.37 | 0 |
Uniball | 0 | 0 | 148.46 | 9.53 | 0.43 | 0 |
Pilot | 0 | 0 | 149.16 | 7.2 | 0.34 | 0 |
Staedtler | 2 | 0 | 130.2 | 12 | 0.98 | 1 |
Dataset Card for keerthikoganti/pens-markers-tabular-dataset-2025
Dataset Details
Dataset Description
This dataset contains measurements and categorical attributes of common pens like Pilot, Bic, Sharpie. It was created as a class exercise for supervised learning on tabular data, supporting both regression by predicting line width and classification such as “thick vs. thin” line.
- Curated by: Fall 2025 24-679 course at Carnegie Mellon University
- Shared by : Keerthi Koganti
- Language(s) (NLP): English
- License: Carnegie Mellon
Uses
Direct Use
Classification: predict Line_Width_Binary thick vs. thin line.
Regression: predict Line_Width_mm from brand/type/ink color and physical attributes.
Created table of different pen types, brands
Out-of-Scope Use
Generalization to all pen marker products or manufacturing.
Any safety-critical or commercial application.
Dataset Structure
This dataset currently includes an original split a single table.Then included an augmented split jitter and categorical bootstrapping with constraint and decision-boundary.
Dataset Creation
Curation Rationale
Provide students with a concrete, physical-world tabular dataset that supports both regression and classification while remaining easy to collect and measure.
Source Data
Data Collection and Processing
Data Collection: Measurements taken from commonly available pens and markers.
Labels: Line_Width_Binary derived from Line_Width_mm using a preset threshold
Processing: Basic cleaning and integer encoding of categorical fields Type, Ink Color.
Who are the source data producers?
Original data: Keerthi Koganti
Augmented data: generated with jitter and categorical bootstrapping with constraint and decision-boundary consistency
Bias, Risks, and Limitations
Small sample size: Limited number of instruments and brands.
Domain bias: Brands,types,colors reflect items readily available to collectors, not the full market.
Recommendations
Use primarily for teaching and demonstration of tabular ML workflows.
If you publish results, disclose the line-width threshold, measurement protocol such as what device used.
Dataset Card Contact
Keerthi Koganti- kkoganti@andrew.cmu.edu
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