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A data exploration project from the University of Michigan that delves into Airbnb listings in New York City. Using Python and Jupyter Notebook, this repository offers visualizations, analyses, and insights into the NYC Airbnb landscape, helping users understand trends, prices, and popular locations.

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Airbnb NYC Explorer 🗽

Welcome to the Airbnb NYC Explorer, a project developed as part of the "Python As An Engineering Tool" course at the University of Michigan-Dearborn. Dive deep into the world of Airbnb listings in New York City, visualize data, and gain insights from our interactive dashboard.

🚀 Features

  • Interactive Dashboard: Explore Airbnb listings through an intuitive and interactive dashboard.
  • Data Visualization: Visualize the geographic distribution of listings, price distributions, neighbourhood analysis, and more.
  • Rent Estimation: Estimate rent based on various parameters like room type, neighbourhood, and address.
  • In-depth Analysis: From hosts with the most reviews to neighbourhood price analysis, get a comprehensive view of the Airbnb landscape in NYC.

📦 Installation & Setup

  1. Clone the Repository:
git clone https://github.com/ItsAlexousd/uofm-airbnb-explorer-nyc.git
cd uofm-airbnb-explorer-nyc
  1. Install Dependencies:
pip install numpy pandas matplotlib plotly dash dash_bootstrap_components seaborn statsmodels scikit-learn
  1. Run the Dashboard:
jupyter notebook airbnb-explorer-nyc.ipynb

Once the server is running, open your web browser and navigate to http://localhost:8050.

📊 Dataset

The dataset, data/AB_NYC_2019.csv, provides a comprehensive view of Airbnb listings in NYC. It includes details such as:

  • Listing ID, name, host ID, host name
  • Neighbourhood, room type, price
  • Minimum nights, number of reviews, availability
  • ... and more!

📝 Documentation

The code is thoroughly documented, with comments explaining each step. For a deep dive into the implementation, refer to the airbnb-explorer-nyc.ipynb file.

🌟 Results & Insights

  • Visualize the geographic distribution of listings on a map.
  • Analyze price distributions with histograms.
  • Understand neighbourhood price dynamics.
  • Discover the top hosts based on reviews.
  • Estimate rents using various parameters.

About

A data exploration project from the University of Michigan that delves into Airbnb listings in New York City. Using Python and Jupyter Notebook, this repository offers visualizations, analyses, and insights into the NYC Airbnb landscape, helping users understand trends, prices, and popular locations.

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