A Study of Pricing and Features with Considerations of Human Error

Abstract: This research paper explores the application of data analytics to an Airbnb dataset containing 74,111 listings, focusing on variables such as room type, accommodates, bathrooms, cancellation policy, cleaning fee, instant bookability, review scores, bedrooms, beds, and log-transformed price. Using Python libraries including pandas, NumPy, and seaborn, we perform exploratory data analysis (EDA) to uncover trends and relationships within the data. The study highlights key statistical insights and identifies potential sources of human error that could impact data quality and analytical and implement KNN algorithm to treat outlier values . The findings provide a foundation for understanding pricing dynamics in the Airbnb market and underscore the importance of addressing human-induced inaccuracies in workflows.