bnbinsight 0.1.0

Airbnb short-term rental pricing analysis package

BnBInsight — Airbnb Short-Term Rental Pricing Analysis

STAT 386 Final Project — A Python package that demonstrates the full data-science pipeline for short-term rental pricing analysis.

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Hypothesis

Primary hypothesis: Listings with more bedrooms, higher ratings, and more amenities have significantly higher nightly prices.

	Statement
H₀	Bedrooms, rating, and amenities count have no relationship with nightly price.
H₁	Bedrooms, rating, and amenities count positively predict nightly price.

Statistical model: log_price ~ bedrooms + rating + amenities_count

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Data Sources

Source	Purpose
Kaggle Airbnb Listings	Primary analysis dataset
AirROI API	Demonstrates original data acquisition pipeline

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Installation

# Clone the repo
git clone https://github.com/mbgardin/BnBInsight.git
cd BnBInsight

# Install dependencies
pip install -r requirements.txt

# Install the package in editable mode
pip install -e .

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Project Structure

airbnb-pricing-project/
├── src/bnbinsight/        # Core package modules
│   ├── data_collectionls.py # Load Kaggle CSV & fetch AirROI API
│   ├── cleaning.py        # Clean price, rating, bedrooms, etc.
│   ├── features.py        # Amenity count, log price, feature selection
│   ├── analysis.py        # Summary stats, OLS regression, flagging
│   ├── visualization.py   # Price distribution, scatter, box plots
│   └── utils.py           # Helper utilities
├── scripts/               # Pipeline scripts
│   ├── fetch_data.py      # Fetch AirROI data
│   ├── clean_data.py      # Clean raw → processed
│   └── run_analysis.py    # Run full analysis
├── app/
│   └── streamlit_app.py   # Interactive dashboard
├── data/
│   ├── raw/               # Raw CSV / JSON
│   └── processed/         # Cleaned dataset
├── docs/                  # Quarto documentation
├── tests/                 # Unit tests
├── pyproject.toml
├── requirements.txt
└── README.md

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How to Run

1. Place your data

Put the Kaggle Airbnb CSV in data/raw/kaggle_airbnb.csv.

2. (Optional) Fetch API data

export AIRROI_API_KEY=your_key_here
python scripts/fetch_data.py

3. Clean the data

python scripts/clean_data.py

4. Run the analysis

python scripts/run_analysis.py

5. Launch the dashboard

streamlit run app/streamlit_app.py

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Run Tests

python -m pytest tests/

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Documentation

Full documentation (built with Quarto) is available on GitHub Pages, as well as the hosted streamlit app: https://bnbinsight.streamlit.app/

https://mbgardin.github.io/BnBInsight/

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License

This project is for educational purposes (BYU STAT 386).