As an avid traveler who has visited several European cities, I have always been interested in finding the best deals on lodging and identifying the optimal times to travel. This project aims to provide insights into the trends and patterns of Airbnb prices in European cities to help travelers like myself identify the best time to book a rental and get the most value for their money.
Airbnb has become a popular alternative to traditional accommodations for travelers around the world. However, the prices of Airbnb rentals can vary significantly depending on the location, season, and other factors. For travelers, it can be challenging to know when to book a rental to get the best deal. Therefore, this project aims to identify trends in Airbnb prices in European cities over the course of a year to find the most optimal time for people to travel on holiday.
Motivating sources:
The dataset used in this project is the "Airbnb Prices in European Cities" dataset available on Kaggle. The dataset contains information about Airbnb listings in 22 European cities, including the listing price, the number of reviews, and the availability of the rental. The dataset also includes geographical information about the listings, such as the latitude and longitude, the neighborhood, and the city.
Data Dictionary:
Explicitly loading the dataset:
import pandas as pd
# Load an example dataset from the zip file (amsterdam weekdays)
df = pd.read_csv('amsterdam_weekdays.csv')
# Display the first 5 rows of the dataset
df.head()
| Unnamed: 0 | realSum | room_type | room_shared | room_private | person_capacity | host_is_superhost | multi | biz | cleanliness_rating | guest_satisfaction_overall | bedrooms | dist | metro_dist | attr_index | attr_index_norm | rest_index | rest_index_norm | lng | lat | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 194.033698 | Private room | False | True | 2.0 | False | 1 | 0 | 10.0 | 93.0 | 1 | 5.022964 | 2.539380 | 78.690379 | 4.166708 | 98.253896 | 6.846473 | 4.90569 | 52.41772 |
| 1 | 1 | 344.245776 | Private room | False | True | 4.0 | False | 0 | 0 | 8.0 | 85.0 | 1 | 0.488389 | 0.239404 | 631.176378 | 33.421209 | 837.280757 | 58.342928 | 4.90005 | 52.37432 |
| 2 | 2 | 264.101422 | Private room | False | True | 2.0 | False | 0 | 1 | 9.0 | 87.0 | 1 | 5.748312 | 3.651621 | 75.275877 | 3.985908 | 95.386955 | 6.646700 | 4.97512 | 52.36103 |
| 3 | 3 | 433.529398 | Private room | False | True | 4.0 | False | 0 | 1 | 9.0 | 90.0 | 2 | 0.384862 | 0.439876 | 493.272534 | 26.119108 | 875.033098 | 60.973565 | 4.89417 | 52.37663 |
| 4 | 4 | 485.552926 | Private room | False | True | 2.0 | True | 0 | 0 | 10.0 | 98.0 | 1 | 0.544738 | 0.318693 | 552.830324 | 29.272733 | 815.305740 | 56.811677 | 4.90051 | 52.37508 |
To solve the problem of identifying the optimal time for people to travel on holiday, I plan to use exploratory data analysis to identify trends and patterns in the Airbnb prices over the course of a year. Specifically, I will visualize the price fluctuations over time and investigate whether certain months or seasons tend to have lower or higher prices. I may also investigate the relationship between price and other features, such as location and room type, to identify any patterns in the data.
As the semester progresses, I will use regression techniques to predict Airbnb prices in European cities based on features such as location, season, and day of the week, and identify optimal times for people to travel based on the trained models.