Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present a more unique, personalized way of experiencing the world. Today, Airbnb became one of a kind service that is used and recognized by the whole world. Data analysis on millions of listings provided through Airbnb is a crucial factor for the company. These millions of listings generate a lot of data - data that can be analyzed and used for security, business decisions, understanding of customers' and providers' (hosts) behavior and performance on the platform, guiding marketing initiatives, implementation of innovative additional services and much more. The dataset is sourced from a Kaggle competition “New York City Airbnb Open Data”(https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data).

The purpose of this project is to implement supervised learning algorithms and predict the price of lodging.

Methods employed: EDA Feature Engineering One Hot Encoding Standard Scaler Polynomial Features Linear Regression Ridge Regression (Cross Validation) Lasso Regression (Cross Validation) ElasticNet Regression (Cross Validation) R-squared and MSE for model evaluation