1. Installation
2. Project Motivation
3. File Descriptions
4. Results
5. Licensing, Authors, and Acknowledgements

You will need the standard data science libraries found in the Anaconda distribution of Python (Pandas, Numpy, Matplotlib, Seaborn, SciKit-Learn...)

For this project, I was interestested in analyzing data from AirBnB homes located in Montreal and Toronto. Specifically, I looked at the following questions:

• How long have hosts been listing properties on Airbnb in both Montreal and Toronto?
• How many days a year do homeowners make their homes available to rent - Montreal Vs Toronto?
• How much do people charge to rent their homes? How does this compare from Montreal to Toronto?
• What is the ideal time to visit Montreal and Toronto ?
• Which Neighborhood is the most rated?
• Which areas of Montreal and Toronto are the most expensive and which area is the best?
• What are the most common room types in Montreal and Toronto?
• What are the most common words used to describe a listing? Are the same words used for Montreal and Toronto homes?
• What are the main factors that influence Airbnb renting price?
• How well can one predict the Airbnb renting price based on the data the two cities?

The following are the files available in this repository:

AIRBNB_Rental_Prices_Analysis_ Montreal_Vs_Toronto.ipynb - a notebook of the analysis performed following the CRISP-DM process

calendar_m.csv and calendar_t.csv - csvs containing home_id, date, availability, and price for each home

listings_m.csv and listings_t.csv - csvs containing id (listing ID), name (name of the listing), host_id (host ID), host_name (name of the host), room_type (listing space type), and price (price in dollars).

reviews_m.csv and reviews_t.csv - csvs containing the home_id, date of review, reviewer_id, reviewer_name, and reviewer comments for the reviewed stays.

The main findings of the code can be found at the blog post published at Analytics Vidhya available here.

Must give credit to AirBnB for the data. You can find the Licensing for the data and other descriptive information for the Montreal and Toronto data on AirbnbInside.