---
id: "fitness-club"
slug: "fitness-club"
name: "Fitness Club"
domain: "Fitness, Sports & Recreation"
taskType: "binary_classification"
numRows: 1500
numColumns: 7
targetColumn: "attended"
primaryMetric: "1 - AUC Error"
primaryScore: "0.1795"
parquetUrl: "https://huggingface.co/datasets/carlahq/demo-tabular-benchmark-containers/resolve/main/containers/fitness_club/01a05c30-7218-79ee-8aae-236e5e9a6655/dataset.parquet"
spreadsheetUrl: "https://docs.google.com/spreadsheets/d/1eVn8Qu02A1PCDY3wIVWyX97HlSJyFtNyrvBKNnChWYU/edit?usp=sharing"
canonicalUrl: "https://carlahq.eu/datasets/fitness-club"
---

# Dataset: Fitness Club (fitness-club)

> The Fitness Club dataset contains 1,500 booking records from the Canadian gym chain GoalZone to predict class attendance and minimize no-show rates for high-demand workout sessions.

## Dataset Overview

| Attribute | Value |
| :--- | :--- |
| **Task Type** | `binary_classification` |
| **Business Domain** | Fitness, Sports & Recreation |
| **Target Column** | `attended` |
| **Rows (Instances)** | 1,500 |
| **Features (Columns)** | 7 |
| **Primary Evaluation Metric** | 1 - AUC Error |
| **Best Benchmark Score** | **0.1795** |

## Business Decision Context
**Context:** GoalZone operates boutique fitness classes capped at 15 or 25 participants. High booking demand frequently leads to fully booked sessions that paradoxically suffer from elevated no-show rates, leaving equipment underutilized and turning away motivated walk-in members.
**Objective:** Predict whether a booked member will attend their scheduled fitness session based on membership tenure, physical profile, booking lead time, and session metadata.
**Decision Impact:** Accurate attendance forecasting allows dynamic overbooking and waitlist backfilling, maximizing studio capacity utilization while preventing revenue loss. False negatives (predicting no-show for an attendee) risk customer dissatisfaction from overbooking, whereas false positives (predicting attendance for a no-show) leave empty spots on studio floors.

## Feature Schema
| Column Name | Data Type | Role | Distinct Values | Missing Values (%) | Description |
| :--- | :--- | :--- | :--- | :--- | :--- |
| `months_as_member` | `numeric` | feature | 72 | 0 | Total number of consecutive months the individual has held an active gym membership at GoalZone. |
| `weight` | `numeric` | feature | 1,241 | 20 | Current recorded body weight of the member in kilograms. |
| `days_before` | `numeric` | feature | 19 | 0 | Number of days in advance the member reserved their spot in the class. |
| `day_of_week` | `string` | feature | 7 | 0 | The day of the week on which the fitness class is scheduled (e.g. Mon, Tue, Wed, Thu, Fri, Sat, Sun). |
| `time` | `string` | feature | 2 | 0 | Scheduled time block of the class, indicating morning (AM) or afternoon/evening (PM) sessions. |
| `category` | `string` | feature | 6 | 0 | Specific workout discipline or class type (e.g. HIIT, Strength, Cycling, Yoga, Aqua). |
| `attended` | `string` | target | 2 | 0 | Binary ground-truth outcome indicating whether the booked member attended the scheduled class session ('Yes' or 'No'). |

## Empirical Benchmark Leaderboard
| Rank | Algorithm / Model | Model Family | Runtime | Accuracy | ROC AUC | F1-Score |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | **nanotabicl Vanilla** | Tabular Foundation Model | PyTorch/Python | **78.08%** | 0.8205 | 0.5794 |
| 2 | **Streaming nanotabicl** | Tabular Foundation Model | PyTorch/Python | **78.07%** | 0.8205 | 0.5792 |
| 3 | **Full TabICLv2 (PyTorch Reference)** | Tabular Foundation Model | PyTorch/Python | **78.07%** | 0.8205 | 0.5792 |
| 4 | **TabPFN v3** | Tabular Foundation Model | PyTorch/Python | **78.01%** | 0.8208 | 0.5797 |
| 5 | **Carla Engine (TabICLv2 WebGPU)** | Tabular Foundation Model | Browser | **77.99%** | 0.8200 | 0.5761 |
| 6 | **Google TabFM v1.0** | Tabular Foundation Model | PyTorch/Python | **77.97%** | 0.8205 | 0.5778 |
| 7 | **EXAONE Tabular** | Tabular Foundation Model | PyTorch/Python | **77.94%** | 0.8203 | 0.5798 |

### Repeated Cross-Validation Fold Scores (TabICLv2 Reference)
| Fold | Train Samples | Test Samples | Metric Score | Latency |
| :--- | :--- | :--- | :--- | :--- |
| Fold 0 | 1000 | 500 | 0.1801 | 764ms |
| Fold 1 | 1000 | 500 | 0.1794 | 689ms |
| Fold 2 | 1000 | 500 | 0.1777 | 705ms |
| Fold 0 | 1000 | 500 | 0.1967 | 693ms |
| Fold 1 | 1000 | 500 | 0.1685 | 696ms |
| Fold 2 | 1000 | 500 | 0.1702 | 694ms |
| Fold 0 | 1000 | 500 | 0.1682 | 707ms |
| Fold 1 | 1000 | 500 | 0.1748 | 688ms |
| Fold 2 | 1000 | 500 | 0.2021 | 712ms |
| Fold 0 | 1000 | 500 | 0.2076 | 693ms |
| Fold 1 | 1000 | 500 | 0.1535 | 709ms |
| Fold 2 | 1000 | 500 | 0.1747 | 696ms |
| Fold 0 | 1000 | 500 | 0.1807 | 725ms |
| Fold 1 | 1000 | 500 | 0.1782 | 698ms |
| Fold 2 | 1000 | 500 | 0.1784 | 707ms |
| Fold 0 | 1000 | 500 | 0.1611 | 699ms |
| Fold 1 | 1000 | 500 | 0.1874 | 704ms |
| Fold 2 | 1000 | 500 | 0.1879 | 701ms |
| Fold 0 | 1000 | 500 | 0.2113 | 695ms |
| Fold 1 | 1000 | 500 | 0.1714 | 700ms |
| Fold 2 | 1000 | 500 | 0.1566 | 703ms |
| Fold 0 | 1000 | 500 | 0.1827 | 694ms |
| Fold 1 | 1000 | 500 | 0.1674 | 702ms |
| Fold 2 | 1000 | 500 | 0.1870 | 695ms |
| Fold 0 | 1000 | 500 | 0.1577 | 716ms |
| Fold 1 | 1000 | 500 | 0.1884 | 706ms |
| Fold 2 | 1000 | 500 | 0.1943 | 722ms |
| Fold 0 | 1000 | 500 | 0.1358 | 683ms |
| Fold 1 | 1000 | 500 | 0.2005 | 713ms |
| Fold 2 | 1000 | 500 | 0.2033 | 695ms |

## Provenance & Downloadable Artifacts
- **Dataset Parquet File:** [Direct Parquet Download](https://huggingface.co/datasets/carlahq/demo-tabular-benchmark-containers/resolve/main/containers/fitness_club/01a05c30-7218-79ee-8aae-236e5e9a6655/dataset.parquet)
- **Google Sheets Interactive Demo:** [Open in Google Sheets](https://docs.google.com/spreadsheets/d/1eVn8Qu02A1PCDY3wIVWyX97HlSJyFtNyrvBKNnChWYU/edit?usp=sharing)
- **Academic Paper / Citation:** [Read Research Paper](https://www.kaggle.com/datasets/ddosad/datacamps-data-science-associate-certification)
- **Canonical HTML Page:** [https://carlahq.eu/datasets/fitness-club](https://carlahq.eu/datasets/fitness-club)