Storytelling Case Study: Airbnb, NYC Description This project is an in-depth analysis of the Airbnb dataset for New York City. The goal is to gain insights into various aspects of the Airbnb market in NYC, including pricing trends, popular neighborhoods, and customer preferences.
Table of Contents Dataset: Information about the dataset used for analysis. Installation: Steps for setting up the project. Data Wrangling: Details on data cleaning and transformation. Exploratory Data Analysis (EDA): Key analyses performed. Visualizations: Charts and graphs to present findings. Conclusion: Summary of insights obtained. Dataset The dataset used is the “AB_NYC_2019.csv” file, containing information about Airbnb listings in NYC. Attributes include neighborhood, room type, price, availability, and customer reviews.
Data Wrangling The data wrangling process involved:
Loading data into a pandas DataFrame. Handling missing values. Cleaning and transforming data. Feature engineering. EDA Highlights Room Types: Distribution analysis across different neighborhood groups. Booking Patterns: Customer preferences based on minimum nights required. Neighborhoods: Availability of listings in different areas. Price Trends: Variation based on room type and neighborhood. Geographical Insights: Price variation across different areas. Key Findings Based on the analysis:
Top neighborhoods for customer bookings: Williamsburg, Bedford-Stuyvesant, and Harlem. Popular neighborhoods with high customer reviews: Allerton, Arverne, and Astoria. Correlation between minimum nights required and bookings. Opportunities for expansion based on listing availability.