This project aims to analyze Airbnb data using MongoDB Atlas, perform data cleaning and preparation, develop interactive geospatial visualizations, and create dynamic plots to gain insights into pricing variations, availability patterns, and location-based trends.
Travel Industry, Property Management and Tourism
- Python scripting
- Data Preprocessing
- Visualization
- EDA
- Streamlit
- MongoDB
- Tableau
Plotly, Seaborn - (To plot and visualize the data) Pandas - (To Clean and maipulate the data) Pymongo - (To store and retrieve the data by connecting with MongoDB Atlas) Streamlit - (To Create Graphical user Interface)
Step 1 : Establish a connection to the MongoDB Atlas database and retrieve the Airbnb dataset.
Step 2 : Clean the Airbnb dataset by handling missing values, removing duplicates, and transforming data types as necessary. Prepare the dataset for EDA and visualization tasks, ensuring data integrity and consistency.
Step 3 : Develop a streamlit web application that utilizes the geospatial data from the Airbnb dataset to create interactive maps.
Step 4 : Use the cleaned data to analyze and visualize how prices vary across different locations, property types, and seasons. Create dynamic plots and charts that enable users to explore price trends, outliers, and correlations with other variables.
Step 5 : Utilize Tableau to create a comprehensive dashboard that presents key insights from your analysis. Combine different visualizations, such as maps, charts, and tables, to provide a holistic view of the Airbnb dataset and its patterns.
Tableau Dashboard link: https://public.tableau.com/app/profile/kaleeswari.s/vizzes
Contributions to this project are welcome! If you encounter any issues or have suggestions for improvements, please feel free to submit a pull request.
📧 Email: [email protected]
🌐 LinkedIn: linkedin.com/in/kaleeswari-s
For any further questions or inquiries, feel free to reach out. We are happy to assist you with any queries.