Airbnb Analysis

Introduction

Airbnb delves into a comprehensive exploration of Airbnb data. Airbnb has revolutionized the travel and property management industry, making it crucial to analyze its data to gain insights into pricing, availability patterns, and location-based trends. This project employs MongoDB Atlas, Streamlit, and data visualization techniques to provide an in-depth analysis.

Table of Contents

1. Key Technologies and Skills
2. Installation
3. Usage
4. Features
5. Contributing
6. License
7. Contact

Key Technologies and Skills

• Python
• Pandas
• MongoDB
• PostgreSQL
• Streamlit
• Plotly
• Tableau

Installation

To run this project, you need to install the following packages:

pip install pandas
pip install pymongo
pip install psycopg2
pip install streamlit
pip install plotly

Features

Data Collection and Preprocessing

• MongoDB Data Retrieval: Acquire the Airbnb dataset from MongoDB for analysis.
• Handling Null and Duplicate Values: Implement preprocessing techniques to address missing data and duplicates.
• ETL and Dataframes: Perform Extract, Transform, Load (ETL) operations to convert the data into structured dataframes for analysis.

Streamlit-based EDA (Exploratory Data Analysis)

• Interactive Data Exploration: Utilize Streamlit to create a user-friendly, interactive interface for exploring Airbnb data.
• Plotly Charts: Employ plotly charts to visualize key insights and trends in the dataset.

Features Analysis

• Property Insights: Analyze the total number of properties based on property type, room type, and bed type.
• Stay Duration Analysis: Investigate the minimum and maximum nights guests typically stay.
• Cancellation Policy Impact: Understand the impact of cancellation policies on booking trends.
• Accommodation Metrics: Explore accommodates, bedrooms, and beds-related statistics.
• Review Analysis: Examine total reviews, average review scores, and the distribution of reviews.
• Bathroom and Pricing Analysis: Investigate bathroom count, pricing, cleaning prices, and extra guest charges.
• Guest Inclusion Trends: Analyze the number of guests included in bookings.
• Host Insights: Explore host-related metrics, including host response time, response rate, and the number of properties hosted.
• Geographic Analysis: Investigate the market and country-level distribution of Airbnb listings.
• Availability Trends: Visualize property availability for the next 30, 60, 90, and 360 days.

Top Host Analysis

Identify and analyze the top 10 hosts based on various features, providing insights into host performance and success.

Visualizations

Utilize Plotly to create interactive and informative visualizations for EDA, making data exploration efficient and insightful.

PowerBI Dashboard

Create a comprehensive dashboard to visually analyze Airbnb data, with a focus on average prices and the number of reviews based on country and room types.

Contributing

Contributions to this project are welcome! If you encounter any issues or have suggestions for improvements, please feel free to submit a pull request.