https://github.com/zen204/airbnb-availability
A machine learning model that predicts Airbnb listing availability, utilizing feature engineering and supervised learning techniques to improve guest experience and optimize host management.
https://github.com/zen204/airbnb-availability
binary-classification data-analysis data-preprocessing data-visualization feature-engineering machine-learning matplotlib model-evaluation nlp pandas predictive-modeling python scikit-learn seaborn supervised-learning
Last synced: 10 days ago
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A machine learning model that predicts Airbnb listing availability, utilizing feature engineering and supervised learning techniques to improve guest experience and optimize host management.
- Host: GitHub
- URL: https://github.com/zen204/airbnb-availability
- Owner: Zen204
- Created: 2024-08-17T20:48:47.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-26T06:35:05.000Z (about 1 year ago)
- Last Synced: 2025-04-02T05:44:21.815Z (10 months ago)
- Topics: binary-classification, data-analysis, data-preprocessing, data-visualization, feature-engineering, machine-learning, matplotlib, model-evaluation, nlp, pandas, predictive-modeling, python, scikit-learn, seaborn, supervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 22 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Predicting Airbnb Availability
This program is designed to predict whether an Airbnb listing is available using machine learning techniques. The project leverages feature engineering, data preprocessing, and supervised learning models to achieve accurate predictions.
## Features
- **Data Preprocessing:** Handles missing data, encodes categorical variables, and scales numerical features for optimal model performance.
- **Feature Engineering:** Extracts relevant features to improve prediction accuracy.
- **Modeling:** Trains and evaluates supervised learning models to predict Airbnb availability.
- **Visualization:** Includes plots to analyze data trends and model performance.
## Requirements
To run the notebook, you need the following dependencies:
- Python 3.7+
- Jupyter Notebook or JupyterLab
- pandas
- numpy
- scikit-learn
- matplotlib
- seaborn
Install the required libraries using the following command:
```bash
pip install -r requirements.txt
```
## Usage
1. Clone the repository:
```bash
git clone https://github.com/yourusername/airbnb-availability-predictor.git
```
2. Navigate to the project directory:
```bash
cd airbnb-availability-predictor
```
3. Launch Jupyter Notebook:
```bash
jupyter notebook Listings_Availability.ipynb
```
4. Run the cells in the notebook sequentially to:
- Load and preprocess the data
- Perform exploratory data analysis (EDA)
- Train and evaluate machine learning models
## Dataset
The dataset includes Airbnb listings with various features such as location, price, and previous booking history. These attributes are used to predict the availability status of a listing.
> **Note:** Ensure you have the required dataset in the specified location before running the notebook. Replace the placeholder path in the notebook with your actual dataset path.
## Results
The notebook provides:
- A detailed analysis of features impacting availability.
- Performance metrics (e.g., accuracy, precision, recall) of the trained models.
- Visualizations to understand data distribution and model predictions.
## Contributing
Contributions are welcome! If you have ideas for improving the project or adding new features, feel free to submit a pull request.
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.
## Acknowledgments
Thank you to my mentors at MIT for all their support throughout this learning process!