Data Sciences 320 Project
Billy Gault
&
Morgan Sterling
Predicting Housing Costs in Washington
Introduction/Problem
The current housing market faces extremely high prices in specific places around the country. This is especially true in Seattle, WA, which has some of the most expensive housing. We plan to integrate two housing datasets and see if we can 1. Use price to predict location and size of the house and 2. Use location and size to predict the price range of the house.
Dataset/Description
We came across two real world data sets consisting of information about housing prices in the state of Washington. Data source 1 consists of 18 columns and 4,600 rows while data source 2 has 15 similar columns and 430 rows.
Goal
The goal is to integrate both data sources and do machine learning (most likely classification) to determine whether or not Washington's housing prices can be predicted by location and size.