A Udacity Project as Part of the Data Science Nanodegree
Better undestand the current state of the real estate market in the middst of a pandemic. When is the right time to buy the dream home in Toronto? In order to answer that I tackled the following questions:
- Distribution od the MLS index among compared to the various types of housing. Which is the best number to look for?
- Is the year to year change in percentage trend raising or declining? How to use that trend to make a tactical decision on when to buy your home.
- Where you should look for your dream home? Identifying the top 5 most expensive neighborhoods for apartment, townhouse, single family detached, and single family attached house.
- Python 3.7.0
numpy == 1.15.1pandas == 0.23.4matplotlib == 2.2.3seaborn == 0.9.0
- MLS.csv - Dataset on which the analysis is performed
- RealEstate_Analysis.ipynb - Jupyter Notebook with the main code for the data analysis
- README file ( where you're at now )
- gitignore for all Python files
In order to better grasp the main results, here is my Medium blog post where I communicated the findings to the tech as well as non tech audience: https://filipova-91822.medium.com/a-look-on-the-recent-torontos-housing-index-e62a22326a8d
A.Wong for providing the dataset on Kaggle. Here is the link https://www.kaggle.com/alankmwong/toronto-home-price-index