Airbnb-Driven-Market-Saturation-and-Forecasting-Visualization-Dashboard - NYC (Full Project Walkthrough)

📊 Objective

The goal of this project is to analyze Airbnb listing data in New York City to:

• Identify pricing patterns by region, neighborhood, and room type
• Understand how Airbnb presence affects long-term rental prices
• Forecast future price trends
• Generate insights for city planners, investors, and policy makers

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🎯 Key Contributions

• Built a full ETL pipeline with Python and PostgreSQL
• Cleaned and enriched raw Airbnb listing data
• Performed EDA and statistical modeling to extract insights
• Visualized key KPIs using Tableau dashboards
• Forecasted monthly average price using regression models
• Wrote complex SQL queries with spatial joins to enrich data

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🧱 Repository Structure

Airbnb-NYC-Analysis/
├── data/
│   ├── AB_NYC_2019.csv                       # Raw dataset
│   ├── AB_NYC_2019_cleaned.csv               # Cleaned output from ETL
│   ├── price_distribution.png                # Output plot from EDA
├── scripts/
│   ├── etl_pipeline.py                       # Data cleaning and feature generation
│   ├── eda_modeling.py                       # EDA, regression, and forecasting
├── sql/
│   └── spatial_queries.sql                   # SQL joins and aggregation
├── README.md                                 # Project documentation
└── requirements.txt                          # Python dependencies
└── Final Tableau Dashboard Project link      # Data Visualization - Tableau Dashboard

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📦 Requirements

Install all dependencies via pip:

pip install -r requirements.txt

requirements.txt

pandas==1.5.3
numpy==1.21.6
matplotlib==3.7.1
seaborn==0.12.2
scikit-learn==1.3.0
statsmodels==0.13.5

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🚀 Installation & Usage

1. Clone the repository:

git clone https://github.com/yourusername/Airbnb-NYC-Analysis.git
cd Airbnb-NYC-Analysis

2. Install dependencies:

pip install -r requirements.txt

3. Run ETL pipeline to clean and preprocess data:

python scripts/etl_pipeline.py

4. Perform EDA and generate plots:

python scripts/eda_modeling.py

5. Use Tableau to load AB_NYC_2019_cleaned.csv and replicate dashboards

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🔍 Modules Breakdown

etl_pipeline.py

• Removes nulls
• Filters invalid price listings
• Creates new features (e.g. month, price/min night)

eda_modeling.py

• Generates boxplots and distribution plots
• Fits a linear regression model
• Runs time-series forecasting

spatial_queries.sql

• Performs joins with zoning and census data
• Aggregates metrics for neighborhoods and boroughs

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📈 KPIs Tracked

• Average Monthly Price
• Listings per Borough
• Top Room Types
• Availability 365
• Repeat Reviewers
• Regression Coefficients

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📊 Dashboard Highlights (Tableau)

• Map of listings by neighborhood
• Filters by price, availability, room type
• KPIs and bar charts by borough and zip code

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❓ Business Questions Answered

• Which boroughs have the highest and lowest prices?
• What listing types generate the most income?
• Are there seasonal price variations by month?
• How does availability vary by region?
• Which areas might be over/under saturated?

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🔍 Example Output

• Boxplot of prices by borough (removing $500+ outliers)
• Linear regression summary with R-squared and coefficients
• Predicted trendline of price vs. month
• Aggregated SQL summary by region

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👩‍💻 Author

Anushka Patil Data Analyst | Python | SQL | PostGreSQL | Tableau | EDA Data Modeling | ETL Pipeline LinkedIn Tableau Dashboard Project link

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💬 Contributions Welcome

Fork the repo, create issues, and submit PRs! This project is open for enhancement and extension with external datasets (e.g. rent control, census, and API integrations).