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https://github.com/raghavendranhp/airbnb-data-analysis
The Airbnb Data Analysis project focuses on analyzing Airbnb data using MongoDB Atlas, Python scripting, data preprocessing, visualization, and interactive geospatial insights. We delve into the world of property management and tourism to uncover trends, pricing variations, and location-based analysis.
https://github.com/raghavendranhp/airbnb-data-analysis
eda jupyter-notebook mongodb numpy pandas powerbi preprocessing
Last synced: 9 days ago
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The Airbnb Data Analysis project focuses on analyzing Airbnb data using MongoDB Atlas, Python scripting, data preprocessing, visualization, and interactive geospatial insights. We delve into the world of property management and tourism to uncover trends, pricing variations, and location-based analysis.
- Host: GitHub
- URL: https://github.com/raghavendranhp/airbnb-data-analysis
- Owner: raghavendranhp
- Created: 2023-10-30T09:32:15.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-02T04:59:30.000Z (about 1 year ago)
- Last Synced: 2024-01-26T11:42:49.704Z (10 months ago)
- Topics: eda, jupyter-notebook, mongodb, numpy, pandas, powerbi, preprocessing
- Language: Jupyter Notebook
- Homepage: https://app.fabric.microsoft.com/reportEmbed?reportId=27833b89-371c-4d7f-8512-4822ba862acd&autoAuth=true&ctid=9f44f2ce-8cae-46bf-930b-0337df8b3945
- Size: 929 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Airbnb Data Analysis Project
Welcome to the Airbnb Analysis project! This project focuses on analyzing Airbnb data to gain valuable insights into pricing variations, availability patterns, and location-based trends in the travel and property management domain. We use MongoDB Atlas, Python scripting, data preprocessing, and Power BI for data visualization to explore the data.
## Project Objectives
The primary objectives of this project are as follows:
1. **Data Retrieval:** Establish a connection to MongoDB Atlas and retrieve the Airbnb dataset efficiently.
2. **Data Cleaning and Preparation:** Clean and prepare the dataset by addressing issues like missing values, duplicates, and data type conversions for accurate analysis.
3. **Price Analysis and Visualization:** Analyze and visualize pricing variations based on location and property type using Power BI.
4. **Availability Analysis:** Investigate availability patterns and occupancy rates and visualize demand fluctuations using Power BI.
5. **Location-Based Insights:** Extract and visualize data for specific regions or neighborhoods, enabling users to understand location-based trends with Power BI.
## Skills Developed
Through this project, you will enhance your skills in the following areas:
- Proficiency in MongoDB Atlas for data management.
- Python data analysis using Pandas and NumPy.
- Data visualization using Power BI.
- Problem-solving skills for data analysis.
## Project Evaluation Metrics
We aim to maintain high-quality code and documentation for this project. The evaluation criteria include:
- Code modularity and maintainability.
- Code portability across different environments.
- A public GitHub repository for version control and collaboration.
- A comprehensive README file explaining the project workflow.
- Adherence to Python coding standards (PEP 8).
## Getting Started
To get started with this project, follow these steps:
1. Clone or fork this repository to your local machine.
2. Refer to the installation instructions in the [Installation](#installation) section to set up the required dependencies.
3. Explore the codebase and dive into the project by following the project workflow described in the [Project Workflow](#project-workflow) section.
## Installation
To run this project, you'll need the following dependencies:
- Python (version 3.6 or higher)
- MongoDB Atlas account
- Required Python packages (listed in `requirements.txt`)
- Power BI Desktop for visualization
## Project Workflow
Note: This project is developed for educational purposes and to showcase data analysis and visualization skills. The Airbnb data used is sample data, and all analyses and visualizations are based on this sample data.
The project workflow is structured into the following key steps:
**MongoDB Connection and Data Retrieval:** Establish a connection to MongoDB Atlas and retrieve the Airbnb dataset efficiently.
**Data Cleaning and Preparation:** Clean and prepare the dataset, addressing issues like missing values, duplicates, and data type conversions for accurate analysis.
**Price Analysis and Visualization:** Analyze and visualize pricing variations based on location and property type using Power BI.
**Availability Analysis:** Investigate availability patterns and occupancy rates and visualize demand fluctuations using Power BI.
**Location-Based Insights:** Extract and visualize data for specific regions or neighborhoods, enabling users to understand location-based trends with Power BI.
## View the Power BI Report
Explore the interactive Power BI report to see the visualizations and insights from this project:
[View PowerBI Report](https://app.fabric.microsoft.com/view?r=eyJrIjoiYTc3ZGEwZDAtMTJhOS00YWU1LWI0MzgtNDUzMGMxNzk3NmViIiwidCI6IjlmNDRmMmNlLThjYWUtNDZiZi05MzBiLTAzMzdkZjhiMzk0NSJ9&pageName=ReportSectione7867f971a5cece3e031)
## Contributing
Contributions and feedback are welcome! If you'd like to contribute to this project, please follow our Contributing Guidelines.
## Project Author,
Raghavendran S
Aspiring Data Scientist
**LinkedIn Profile:** [Raghavendran Sundararajan](https://www.linkedin.com/in/raghavendransundararajan/)
Happy Analyzing!