Airbnb Analysis with Python

Explore the dynamics of short-term rentals through data. Airbnb has transformed the way people travel and find accommodation, making it a rich subject for data analysis. In this project, you’ll work with an Airbnb dataset that includes details such as listings, prices, locations, and host information. By combining Pandas for data manipulation and Seaborn for visualization, you’ll uncover trends and insights into the short-term rental market. You’ll begin by loading and inspecting the dataset, handling missing values, and preparing variables for analysis. With Pandas, you’ll filter listings by city or neighborhood, compute average prices, and examine distributions of availability or reviews. You’ll also create calculated columns such as price per night or reviews per month to deepen the analysis. Next, you’ll apply Seaborn visualizations to bring patterns into focus. Histograms and box plots will reveal the distribution of prices, scatter plots will highlight relationships such as location vs. price, and bar plots will compare availability or ratings across neighborhoods. You’ll analyze questions like: Which neighborhoods have the highest average prices? How do reviews correlate with host activity? What patterns exist in listing availability across time? By the end of this project, you will be able to: Clean and organize Airbnb datasets with Pandas. Compute and summarize key metrics like average prices and availability. Create Seaborn visualizations to explore price, location, and review patterns. Interpret results to explain market dynamics in short-term rentals. This project blends data manipulation and visualization to give you practical experience with real-world datasets. By analyzing Airbnb listings, you’ll gain both technical skills and valuable insights into how digital platforms shape modern travel.