About the Data

To load the training data in your jupyter notebook, use the below command:

import pandas as pd
airbnb_data = pd.read_csv("https://raw.githubusercontent.com/dphi-official/Datasets/master/airbnb%20_data/airbnb_listing_train.csv" )

The data sets have not been cleaned since this is an important step in creating a predictive model. Note also that there are many opportunities to engineer your own features. It is allowed to merge your data with other online data as long as you can make sure that your model also works on the validation data. Note also that the data sets are large so be aware of overfitting your models.

Feature Description

id: The id of each lodge/home/listing
name: The name/description of the lodge/home
host_id: The id of the host
host_name: Name of the host
neighbourhood: Name of the neighbourhood place
neighbourhood_group: Group in the neighbourhood
latitude: Latitude of the location
longitude: Longitude of the location
room_type: Type of the room that consumer booked, for example, private room or an entire home, etc.
minimum_nights: The minimum number of nights customer will stay
number_of_reviews: Number of reviews given to the lodge/home
last_review: The date of the last review given to the lodge/home
reviews_per_month: Average reviews per month
calculated_host_listings_count: The count of the listing that each host has
availability_365: The number of days (out of 365 days) for which lodge/home is available
price: Price for the lodging/homestays in USD - the target variable

Acknowledgement

This dataset is part of Airbnb Inside, and the original source can be found here.

Predict the price of an Airbnb listing

Challenge Starts

Challenge Ends