##Airbnb EDA and Data Visualization - New York 2024
##Project Overview
This project explores Airbnb listings in New York (2024) using Pandas, Matplotlib, and Seaborn. The goal is to analyze price distribution, neighborhood trends, and host behavior through data visualization techniques.
##Dataset
The dataset used is the New York Airbnb Listings 2024. It includes details like listing price, neighborhood, host information, and availability.
##Key Features
- Data Cleaning
Removed missing values
Dropped duplicate entries
Converted data types for accurate analysis
- Exploratory Data Analysis (EDA)
Univariate Analysis: Price distribution, identifying outliers
Bivariate Analysis: Correlations between price, location, and availability
Host & Room Type Insights
- Data Visualization
Boxplots for price outliers
Histograms for price distribution
Bar charts for neighborhood-wise listing count
Scatter plots for price vs. availability trends
##Tech Stack
Python
Pandas for data manipulation
Matplotlib and Seaborn for visualization
##How to Run the Project
- Download the Repository
Click on the "Code" button in GitHub.
Select "Download ZIP" and extract the files.
- Open in VS Code
Open VS Code and navigate to the project folder.
- Install Dependencies (if needed)
pip install pandas matplotlib seaborn
- Run the Script
Open Airbnb.ipython or Airbnb.py in VS Code and execute the analysis.
##Project Insights
Certain neighborhoods have significantly higher prices.
Some listings show unusual pricing patterns, requiring further investigation.
Host behavior and room type impact pricing strategy.
##Repository Structure
Airbnb-EDA
Airbnb.ipython # Python script for analysis in VS Code
datasets.csv # Dataset used
README.md # Project Documentation