AirBnb-Project

AirBnb-Python Project

AirBnB-Buisness-Analysis

This project focuses on analyzing Airbnb datasets using Python and Pandas. The Jupyter Notebook (airbnb.ipynb) provides insights into trends, pricing, customer preferences, and other key metrics to help understand Airbnb's market dynamics.

Kaggle Dataset link - : https://www.kaggle.com/datasets/mysarahmadbhat/airbnb-listings-reviews

Key Features

1.Data Cleaning and Preparation :

1. Handling missing values
2. Renaming columns for consistency
3. Converting data types for better processing.

2.Exploratory Data Analysis (EDA):

1. Analysis of pricing trends across different locations
2. Insights into room types and their availability
3. Identification of popular listings based on reviews and occupancy.

3. Visualization:

1. Charts and graphs to illustrate key insights.
2. Histograms for price distributions.
3. Bar charts for room type comparisons.

4. Insights:

1. Price Trends: Listings in popular tourist areas tend to have higher prices compared to suburban or rural areas.

2. Availability Patterns: Properties with higher review counts tend to have higher occupancy rates.

3. Customer Preferences: Guests prefer entire homes/apartments over shared rooms, indicating a demand for privacy.

4. Seasonal Demand: Prices and bookings peak during holiday seasons and major local events.

5. Host Engagement: Listings with responsive hosts and better amenities receive more positive reviews and higher bookings.

Tools and Libraries Used:

1. Python: Core programming language for data analysis.
2. Pandas: For data manipulation and cleaning.
3. Matplotlib/Seaborn: For data visualization.
4. Jupyter Notebook: Interactive environment for combining code, visualizations, and narrative text.
5. Kaggle : To use the AirBnB Listings & Reviews Dataset .

Future Enhancements :

1. Integration of machine learning models to predict Airbnb prices.
2. Automated dashboards for real-time data visualization.
3. Deeper analysis of customer reviews for sentiment insights.