Exploring Airbnb Market Trends

This project analyzes Airbnb listing data from New York City to uncover insights about room types, review activity, and pricing trends. The analysis involves combining data from multiple file formats — CSV, TSV, and Excel — to build a unified view of the 2019 Airbnb market. This project was completed using DataCamp’s Datalab environment.

🎯 Project Objectives

• Merge data from multiple sources (CSV, Excel, TSV)
• Determine the earliest and most recent review dates
• Count the number of private room listings
• Calculate the average nightly price of listings

🗃️ Dataset Overview

The data comes from three files:

File	Description
airbnb_price.csv	Listing prices and neighborhoods
airbnb_room_type.xlsx	Listing descriptions and room types
airbnb_last_review.tsv	Host names and last review dates

Unified dataset columns:

Column	Description
listing_id	Unique identifier of listing
price	Nightly listing price (in USD)
nbhood_full	Full neighborhood and borough name
description	Listing description
room_type	Type of room offered (e.g., private room)
host_name	Name of the host
last_review	Date of the most recent review

🔍 Key Findings

• 📅 Listings were reviewed between January 1, 2019 and July 9, 2019
• 🛏️ There were 11,356 private rooms listed in the dataset
• 💲 The average price of a listing was $141.78 per night

🛠️ Tools Used

• Python
• pandas for merging and transforming datasets
• NumPy for numeric processing
• Worked with CSV, Excel, and TSV file formats

📌 How to Use

1. Clone or download this repository
2. Open the notebook Exploring_Airbnb_Market_Trends.ipynb in Jupyter or any compatible environment
3. Run the cells to reproduce the analysis
4. Modify the logic to explore other cities, room types, or time windows

✍️ Author

Project by Achraf Salimi — part of an ongoing journey to build and showcase data skills.