https://github.com/anushkapatil0727/airbnb-driven-market-saturation-and-forecasting-visualization-dashboard
Analyzed Airbnb's impact on NYC rentals using Python, SQL, and Tableau. Built an ETL pipeline, performed forecasting with scikit-learn, and created interactive dashboards to visualize pricing trends and listing saturation.
https://github.com/anushkapatil0727/airbnb-driven-market-saturation-and-forecasting-visualization-dashboard
analystics business-analytics etl-pipeline kpi linear-regression matplotlib-python pandas-python postgresql problem-solving seaborn-python sklearn statsmodels tableau-dashboards visualization
Last synced: 3 months ago
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Analyzed Airbnb's impact on NYC rentals using Python, SQL, and Tableau. Built an ETL pipeline, performed forecasting with scikit-learn, and created interactive dashboards to visualize pricing trends and listing saturation.
- Host: GitHub
- URL: https://github.com/anushkapatil0727/airbnb-driven-market-saturation-and-forecasting-visualization-dashboard
- Owner: anushkapatil0727
- Created: 2025-07-08T21:58:07.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-08T22:04:37.000Z (3 months ago)
- Last Synced: 2025-07-08T23:21:00.872Z (3 months ago)
- Topics: analystics, business-analytics, etl-pipeline, kpi, linear-regression, matplotlib-python, pandas-python, postgresql, problem-solving, seaborn-python, sklearn, statsmodels, tableau-dashboards, visualization
- Language: Python
- Homepage:
- Size: 2.71 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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
---
## 🎯 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
---
## 🧱 Repository Structure
```bash
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
```
---
## 📦 Requirements
Install all dependencies via pip:
```bash
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
```
---
## 🚀 Installation & Usage
1. Clone the repository:
```bash
git clone https://github.com/yourusername/Airbnb-NYC-Analysis.git
cd Airbnb-NYC-Analysis
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Run ETL pipeline to clean and preprocess data:
```bash
python scripts/etl_pipeline.py
```
4. Perform EDA and generate plots:
```bash
python scripts/eda_modeling.py
```
5. Use Tableau to load `AB_NYC_2019_cleaned.csv` and replicate dashboards
---
## 🔍 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
---
## 📈 KPIs Tracked
* Average Monthly Price
* Listings per Borough
* Top Room Types
* Availability 365
* Repeat Reviewers
* Regression Coefficients
---
## 📊 Dashboard Highlights (Tableau)
* Map of listings by neighborhood
* Filters by price, availability, room type
* KPIs and bar charts by borough and zip code
---
## ❓ 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?
---
## 🔍 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
---
## 👩💻 Author
**Anushka Patil**
Data Analyst | Python | SQL | PostGreSQL | Tableau | EDA Data Modeling | ETL Pipeline
[LinkedIn](https://www.linkedin.com/in/anushkapatil272000/)
[Tableau Dashboard Project link](https://public.tableau.com/views/AirbnbMarketAnalysisforNYC/Dashboard1?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link)
---
## 💬 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).