This project explores the Oslo listing market using Airbnb data.

1. airbnb_oslo.ipynb

Jupyter Notebook including data wrangling, visualization, and text analysis to answer the following questions.

• Has there been growth in Oslo rental market over the years?
• When should people visit Oslo? When is the most popular month of year and how is the availability, and the price?
• Where should people stay in Oslo? Can I say something about location, price, availability, and atmosphere of different neighborhood?

2. Data

Description

• reviews.csv: Summary Review data and Listing ID (to facilitate time based analytics and visualisations linked to a listing)
• calendar.csv: Detailed Calendar data for listings in Oslo
• listings.csv: Detailed Listings data for Oslo

Please download the data sets locally first using below links.

• http://data.insideairbnb.com/norway/oslo/oslo/2019-09-28/visualisations/reviews.csv
• http://data.insideairbnb.com/norway/oslo/oslo/2019-09-28/data/calendar.csv.gz
• http://data.insideairbnb.com/norway/oslo/oslo/2019-09-28/data/listings.csv.gz

Datasets used in the projects were scraped 28 September 2019

3. Article about the project

• https://medium.com/@kimjinhaha/when-should-you-visit-oslo-and-where-should-you-stay-f4e2f2e400ab

4. References and Inspiration

• https://towardsdatascience.com/nlp-for-beginners-cleaning-preprocessing-text-data-ae8e306bef0f
• https://www.kaggle.com/aashirwad01/airbnb-analysis
• https://sanjayasubedi.com.np/nlp/nlp-feature-extraction/
• https://www.kaggle.com/divsinha/sentiment-analysis-countvectorizer-tf-idf