KNN Regression, Regression Trees, & GLM
United States housing prices have fluctuated over the past century. Different parts of the country have gradually become more expensive to live in as a result of changes in economic conditions. Housing prices reflect the estimated values of homes in relation to their physical features and geographical locations. The goal of the analysis was to use regression analysis in helping to identify which characteristics have the greatest effects on home value and provide this information to prospective homeowners.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
What things you need to install the software and how to install them
R Studio
Libraries: 'tidyverse','rpart','rpart.plot','Metrics','forecast','caret',
'ggplot2', 'FNN', 'fastDummies','dataPreparation','reshape2','corrplot'
The data was sourced from Kaggle, consisting of 21 variables and 21,613 observations. The dataset describes features and prices of homes within the area of King County, Washington during the time period of May 2014-2015, which includes Seattle and the surrounding cities. As the most populous county in Washington at 2.2 million, and the 12th most populated county in the United States, King County offers diverse housing to its many inhabitants. The analysis aims to examine the relationship between the housing features and the sales price within the area.
- Adrian Leung - Initial work - (https://github.com/leungad)