ccm/2025-24679-tabular-autolguon-predictor
Tabular Classification
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About how many hours per week do you spend listening to music?
float64 | Approximately how many songs are in your music library?
int64 | Approximately how many playlists have you created yourself?
int64 | How often do you share music with others?
int64 | Which decade of music do you listen to most?
int64 | How often do you attend live music events?
int64 | Do you prefer songs with lyrics or instrumental music?
int64 | Do you usually listen to music alone or with others?
int64 |
|---|---|---|---|---|---|---|---|
3
| 500
| 10
| 1
| 5
| 1
| 1
| 0
|
20
| 500
| 10
| 1
| 4
| 1
| 1
| 0
|
20
| 50
| 10
| 1
| 4
| 1
| 0
| 0
|
24
| 600
| 12
| 0
| 4
| 0
| 1
| 0
|
20
| 205
| 15
| 2
| 5
| 1
| 0
| 0
|
50
| 100
| 6
| 1
| 4
| 2
| 1
| 0
|
7
| 200
| 4
| 0
| 4
| 1
| 0
| 0
|
12
| 3,000
| 40
| 3
| 4
| 2
| 0
| 0
|
40
| 1,000
| 2
| 1
| 4
| 1
| 1
| 0
|
8
| 1,000
| 15
| 1
| 4
| 1
| 2
| 0
|
15
| 1,200
| 6
| 3
| 5
| 4
| 0
| 0
|
6
| 40
| 3
| 1
| 4
| 0
| 1
| 0
|
10
| 30
| 1
| 1
| 5
| 0
| 1
| 1
|
12
| 2,000
| 80
| 4
| 5
| 2
| 0
| 0
|
15
| 400
| 6
| 1
| 1
| 2
| 0
| 0
|
15
| 4,000
| 100
| 3
| 4
| 2
| 1
| 0
|
40
| 10,000
| 300
| 2
| 5
| 3
| 0
| 0
|
40
| 3,000
| 12
| 4
| 4
| 0
| 2
| 1
|
14
| 50
| 1
| 1
| 5
| 1
| 0
| 0
|
10
| 2,000
| 5
| 3
| 4
| 3
| 0
| 0
|
74
| 6,185
| 421
| 3
| 4
| 3
| 1
| 0
|
6
| 8,000
| 9
| 3
| 3
| 2
| 1
| 1
|
20
| 2,000
| 19
| 3
| 2
| 2
| 1
| 1
|
1
| 500
| 2
| 1
| 4
| 1
| 0
| 0
|
25
| 3,500
| 12
| 3
| 4
| 2
| 1
| 0
|
15
| 400
| 1
| 1
| 4
| 1
| 1
| 0
|
20
| 2,206
| 15
| 3
| 5
| 2
| 0
| 0
|
3
| 300
| 10
| 1
| 4
| 1
| 0
| 0
|
18
| 40
| 5
| 3
| 4
| 1
| 0
| 1
|
20
| 1,000
| 10
| 2
| 4
| 1
| 0
| 0
|
7
| 500
| 10
| 2
| 5
| 2
| 0
| 0
|
30
| 2,700
| 50
| 4
| 0
| 3
| 1
| 0
|
10
| 1,000
| 20
| 2
| 4
| 2
| 0
| 0
|
3.5
| 80
| 4
| 1
| 2
| 0
| 0
| 0
|
1
| 200
| 5
| 0
| 3
| 1
| 2
| 0
|
10
| 100
| 2
| 1
| 5
| 0
| 1
| 0
|
Dataset Card for ccm/2025-24679-tabular-dataset
This dataset captures self-reported music listening behaviors and preferences, including listening hours, library size, playlist creation, sharing habits, preferred decades, and social context (alone vs. with others). It was created as a class exercise in data collection and augmentation.
Dataset Details
Dataset Description
This dataset captures self-reported music listening behaviors and preferences, including listening hours, library size, playlist creation, sharing habits, preferred decades, and social context (alone vs. with others). It was created as a class exercise in data collection and augmentation.
- Curated by: Fall 2025 24-679 course at Carnegie Mellon University
- Shared by [optional]: Christopher McComb
- License: MIT
Uses
Direct Use
The direct intended use for this dataset is to perform classification and regressio tasks related to the labels (i.e., classifying solo/group listening preferences, regressing for hours of music listened per week)
Out-of-Scope Use
This dataset will not work well for most applications due to its size and synthetic nature. It is intended only for training.
Dataset Structure
This dataset contains both an original split (which contains original photographs) and an augmented split (which augmented images based on the original split).
Dataset Creation
Curation Rationale
The dataset was created to provide students with a structured but low-stakes dataset for experimenting with supervised learning workflows. Music listening was chosen because it is familiar, engaging, and relatively easy to survey.
Source Data
The source data was collected on campus at CMU
Data Collection and Processing
Responses were collected via short in-class surveys.
Who are the source data producers?
- Original data: Students in the course, who self-reported their music listening habits.
- Augmented data: Automatically generated by course instructors and teaching assistants.
Bias, Risks, and Limitations
- Small sample size: Only 36 original respondents; dataset is not representative.
- Synthetic data: Augmented examples may not reflect real-world distributions.
- Demographic skew: Original responses come from a limited population (engineering students at a U.S. university).
Recommendations
- Use only for experimentation and demonstration, not for scientific or commercial conclusions.
- Treat augmented data cautiously, especially for evaluating model generalization.
- When teaching, emphasize dataset limitations as part of the learning process.
Dataset Card Contact
Christopher McComb (Carnegie Mellon University) — [email protected]
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Number of rows:
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Models trained or fine-tuned on ccm/2025-24679-tabular-dataset