AirBNB Price Prediction Project

Predicting Airbnb house prices in Barcelona using R and the tidymodels framework.

View the Live App on Render

Introduction

This project leverages R and the tidymodels ecosystem to predict Airbnb house prices in Barcelona. The workflow includes data cleaning, feature engineering, model selection, and evaluation, aiming to provide actionable insights for property owners and speculators.

Key Questions Addressed

  1. What are the main factors influencing Airbnb listing prices in Barcelona?
  2. How accurately can we predict listing prices using available data?
  3. What insights can be drawn for property owners and investors?

Project Workflow

  • Data Cleaning: Handling missing values, outliers, and inconsistent entries.
  • Feature Engineering: Creating new variables and transforming existing ones for better model performance.
  • Model Selection: Comparing multiple regression models using tidymodels.
  • Evaluation: Assessing model performance using metrics such as RMSE and R².
  • Deployment: Making the model accessible via a web app.

Technologies Used

  • R
  • tidymodels
  • dplyr, ggplot2
  • Machine Learning
  • Web Deployment (Render)

Last updated: April 15, 2025