The aim of this project is to predict the prices of Airbnb listings. The machine learning model takes into account factors such as property features, location, and date range to provide users with reference prices.
In this project, we are working with a dataset that contains information about Airbnb listings in the US. Our goal is to predict the price of a stay in these listings. The dataset, which can be found at the following source: link, consists of 74411 listings and 29 columns. One of the columns, log_price, is the target variable that we are trying to predict.
We will not be using certain columns such as ID and thumbnail URL as features, leaving us with 26 columns to process and consider as potential features. The dataset does not provide specific information about the data itself. However, we assume that the price (or log_price) we are predicting represents the general pricing per one-night stay in the listing, in USD. This price does not include additional fees like cleaning or Airbnb service fees, and it is not specific to particular dates or seasons.
The dataset covers listings from six cities across the US: NYC, LA, San Francisco, Washington DC, Boston, and Chicago.
Data Exploration: Load the dataset and conduct initial exploratory data analysis (EDA) to gain an understanding of the data structure. Check for missing values, explore feature distributions, and analyze correlations. This step will provide valuable insights into the dataset and identify any necessary data preprocessing steps.
Feature Selection: Since some columns will not be used as features, remove those columns from the dataset. Consider the remaining 26 columns as potential features for predicting the price. Further analyze and select the most relevant features based on their impact on the target variable.
Data Preprocessing: Clean the data by addressing missing values, outliers, and any inconsistencies. Convert categorical variables into numerical representations using techniques such as one-hot encoding or label encoding. Standardize or normalize numerical features if needed.
Feature Engineering: Generate additional features from existing ones if it improves the predictive power of the model.
By following these steps, you can progress with your project to predict the price of stay in Airbnb listings based on the provided dataset.