This project focuses on analyzing Airbnb data to gain insights into pricing variations, availability patterns, and location-based trends in the travel industry, property management, and tourism domain. The project uses MongoDB Atlas for data storage and retrieval, Python for data preprocessing, visualization, and interactive web application development with Streamlit. Additionally, it employs PowerBI or Tableau to build a comprehensive dashboard for presenting key insights.
- Python scripting
- Data preprocessing
- Data visualization
- Exploratory Data Analysis (EDA)
- Streamlit for interactive web application
- MongoDB Atlas for data storage
- PowerBI or Tableau for dashboard creation
The objectives of this project are:
- Establish a MongoDB connection, retrieve the Airbnb dataset, and ensure efficient data retrieval for analysis.
- Clean and prepare the dataset, addressing missing values, duplicates, and data type conversions for accurate analysis.
- Develop a Streamlit web application with interactive maps showcasing the distribution of Airbnb listings, allowing users to explore prices, ratings, and other relevant factors.
- Conduct price analysis and visualization, exploring variations based on location, property type, and seasons using dynamic plots and charts.
- Investigate location-based insights by extracting and visualizing data for specific regions or neighborhoods.
- Create interactive visualizations that enable users to filter and drill down into the data.
- Build a comprehensive dashboard using Tableau or Power BI, combining various visualizations to present key insights from the analysis.