Analysis of the King Kountry Houses dataset done as an exercise to practice Supervised Machine Learning and other Data Science techniques (EDA, Data Cleanning, Univariate Analysis, Bivariate Analysis, etc...)
Your mission is to delve into a dataset of house sale prices for King County, including Seattle, spanning one year from May 2014 to May 2015. This project encompasses various computational tasks such as data loading, visualization, calculating returns, and portfolio analysis, tailored to the real estate domain. It aims to enhance your Python skills, deepen your understanding of real estate financial data, and hone your analytical prowess. Are you ready to embark on this analytical journey through the housing market?
All the project is documented the kingkountry.ipynb file.
A review of of the project at the presentation KingKountryHouses.pdf including the answer to the initial questions, implementationd details, obstacles and conclusions.
This work is licensed under a Creative Commons Attribution 4.0 International License.
