Predicting Airbnb listing prices can be a complex yet rewarding task. Whether you’re a potential host aiming to set competitive rates or a guest looking for budget-friendly stays, understanding how prices are determined can give you valuable insights. In this blog post, we’ll walk you through the steps to create a model that predicts Airbnb prices using a dataset, and we’ll present the results using geographical heatmaps, bar charts, and price distribution plots. Let’s dive in!
Here’s a step-by-step implementation of an Airbnb Price Prediction model using the New York City Airbnb dataset, along with interactive visualizations and a GUI using ipywidgets.
We’ll walk through the following steps:
- Load and explore the dataset.
- Perform basic preprocessing and exploratory data analysis (EDA).
- Train a machine learning model for price prediction.
- Create visualizations: geographical heatmaps, bar charts, and price distribution plots.
- Build an interactive GUI using
ipywidgets.