🏋️ Gym Class Heroes

What actually predicts workout performance — duration, workout type, or something else?

This project runs a full exploratory data analysis on a fitness/exercise dataset (body metrics, workout type, session duration, heart rate, calories burned, experience level) using nothing but statistical plots. The goal isn't modeling — it's building fast, fluent visual intuition for a dataset using Seaborn's statistical plotting toolkit.

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🎯 Project Questions

• Does workout duration actually correlate with calories burned, or does workout type matter more?
• How does heart rate vary across workout types — any clear outliers?
• Does session duration differ meaningfully by experience level (beginner/intermediate/advanced)?
• Which numeric variables are most correlated with each other overall?

Findings

1. Session duration vs Calories burned with workout type: workout type doesn't matter that much but there is a strong correlation(0.91) between duration and calories burned.
2. Heart rate distribution according to workout type: strength workout type has the highest range of heart rate variation, but overall fairly similar across workout types.
3. Session duration according to experience level session duration is fairly similar across beginner and intermediate experience levels, but advanced users have a higher average session duration.
4. Correlation matrix: the highest correlation is between session duration and calories burned (0.91), followed by Calories_Burned and Fat_Percentage (-0.60).

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📁 Repository Structure

gym-class-heroes/
├── data/
│   ├── raw/                 # Untouched source dataset(s)
│   └── processed/           # Cleaned, analysis-ready DataFrame
│
├── notebooks/                # Exploratory analysis, step-by-step, one notebook per phase
│   ├── 01_load_explore.ipynb
│   ├── 02_distributions_box_violin.ipynb
│   ├── 03_pairplot_heatmap.ipynb
│   └── 04_categorical_styling.ipynb
│
├── src/                       # Reusable code, imported by notebooks/scripts (not copy-pasted)
│   ├── __init__.py
│   ├── analysis.py            # Analysis for project questions
│   └── plots.py               # Reusable theme setup
│
├── tests/                     # Unit tests for src/ functions
│   ├── __init__.py
│   └── test_clean.py
│
├── outputs/                   # Final saved PNGs — the actual EDA report
│   └── quesions/ 
│
├── .gitignore
├── requirements.txt
└── README.md

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🛠️ Setup

git clone https://github.com/hexsyro/gym-class-heroes.git
cd gym-class-heroes
python -m venv venv
source venv/bin/activate       # Windows: venv\Scripts\activate
pip install -r requirements.txt

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🚀 Usage

1. Analysis (fills outputs/questions/)

   python -m src.analysis

2. Explore in notebooks, in order, starting with notebooks/01_load_explore.ipynb
3. Final charts get saved to outputs/ as the polished EDA report

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📊 Skills Practiced

Skill	Where
Statistical plots (histplot, boxplot, violinplot)	notebooks/03
Pairplots and correlation heatmaps	notebooks/04
Categorical plots (barplot, countplot)	notebooks/05
Styling and themes for faster EDA	src/plots.py, notebooks/05

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📈 Sample Finding

"Workout session duration explains more variance in calories burned than workout type does — HIIT sessions burn nearly as much in 2 hours as strength session does in same time (1800 Calories)."

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📄 Data Sources

• Fitness/exercise dataset: Gym Members Exercise Dataset : with body metrics, workout type, heart rate, and calories already included

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🔭 Possible Extensions

• Facet the pairplot by gender or experience level using hue=
• Add a sns.jointplot() for the single strongest correlation found
• Build a one-page "EDA report" combining the best 4-5 charts with consistent styling