https://github.com/msthamizh/airbnb_analysis

Developing a Streamlit application enabling users to explore and analyze Airbnb listing data. This application allows users to interactively visualize geospatial distributions of listings, analyze pricing trends, and explore availability patterns across different locations. Integrates MongoDB Atlas for data storage and PowerBi for advanced insights
https://github.com/msthamizh/airbnb_analysis

data-analysis data-cleaning data-visualization json mongodb pandas-dataframe plotly powerbi python streamlit

Last synced: 7 days ago
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Developing a Streamlit application enabling users to explore and analyze Airbnb listing data. This application allows users to interactively visualize geospatial distributions of listings, analyze pricing trends, and explore availability patterns across different locations. Integrates MongoDB Atlas for data storage and PowerBi for advanced insights

• Host: GitHub
• URL: https://github.com/msthamizh/airbnb_analysis
• Owner: MSThamizh
• Created: 2024-10-21T16:20:57.000Z (16 days ago)
• Default Branch: main
• Last Pushed: 2024-10-21T16:57:59.000Z (16 days ago)
• Last Synced: 2024-10-22T05:38:31.892Z (16 days ago)
• Topics: data-analysis, data-cleaning, data-visualization, json, mongodb, pandas-dataframe, plotly, powerbi, python, streamlit
• Language: Jupyter Notebook
• Homepage:
• Size: 16.4 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Airbnb Data Analysis and Visualization

This project focuses on analyzing Airbnb listing data to uncover trends in pricing, availability, and location-based insights. By leveraging tools such as MongoDB Atlas for data storage, Streamlit for building an interactive application, and Tableau/Power BI for advanced visualizations, this project aims to provide a comprehensive view of the Airbnb market.

## Problem Statement

The goal of this project is to develop a **Streamlit application** that enables users to explore and analyze Airbnb data efficiently. The application should provide the following features:

- **Data Retrieval**: Retrieve and manage Airbnb listings data from MongoDB Atlas, including geospatial data, pricing information, availability patterns, and neighborhood insights.

- **Data Cleaning**: Address missing values, duplicates, and convert data types to ensure the dataset is ready for analysis.

- **Geospatial Visualization**: Allow users to interact with maps showing Airbnb listing locations and other geospatial insights.

- **Price Analysis**: Explore pricing trends based on location, property type, and seasonal variations.

- **Availability Patterns**: Visualize availability and occupancy trends across different seasons or months.

- **Location-Based Insights**: Provide neighborhood-level insights into pricing, reviews, and listing density.

- **Dashboard Creation**: Offer advanced dashboards using Power BI with interactive filters and visualizations.

## Workflow

The workflow for this project can be summarized as follows:

1. **Data Retrieval from MongoDB**: Fetch data from the Airbnb dataset stored in MongoDB Atlas. This includes geospatial data, prices, availability, and more.

2. **Data Cleaning and Preparation**: Clean the data by addressing missing or incorrect values and converting them into the correct format.

3. **Geospatial Analysis**: Perform geospatial analysis using libraries like Geopandas and Folium to visualize listings on a map.

4. **Price and Availability Analysis**: Analyze pricing and availability patterns using dynamic charts and visualizations.

5. **Streamlit Web Application**: Build an interactive web app for users to explore data through dynamic filters and maps.

6. **Power BI Dashboards**: Combine insights into a comprehensive dashboard for presenting key findings.

## Features

- **MongoDB Integration**: Retrieve real-time Airbnb listing data from MongoDB Atlas for analysis.

- **Interactive Maps**: Geospatial visualizations allow users to explore Airbnb listings on an interactive map.

- **Dynamic Price Analysis**: Visualize pricing trends over time, location, and property type.

- **Availability and Occupancy Analysis**: Explore availability trends by season, month, or neighborhood.

- **Neighborhood Insights**: Provide location-specific insights into Airbnb pricing, listing density, and availability.

- **Tableau/Power BI Dashboards**: Advanced dashboards for presenting data insights in an interactive format.

## Technologies Used

- **Python**: Main programming language for data processing and application development.

- **MongoDB Atlas**: NoSQL database used to store Airbnb data.

- **Streamlit**: Python framework used to build the interactive web application.

- **Plotly**: For creating interactive charts and dynamic visualizations.

- **Power BI**: Tools for building comprehensive, interactive dashboards.

- **Pandas/Numpy**: For data manipulation and analysis.

## References

- **Python**: [https://docs.python.org/3/](https://docs.python.org/3/)

- **MongoDB Documentation**: [https://www.mongodb.com/](https://www.mongodb.com/)

- **Streamlit Documentation**: [https://docs.streamlit.io/library/api-reference](https://docs.streamlit.io/library/api-reference)

- **Pandas Dataframe**: [https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)

- **Plotly Documentation**: [https://plotly.com/python/](https://plotly.com/python/)

- **Dataset**: [https://docs.google.com/document/d/1SYlU0Wq4Ay-z_CTU3qviTwZd_eDp0vIB/edit](https://docs.google.com/document/d/1SYlU0Wq4Ay-z_CTU3qviTwZd_eDp0vIB/edit)