Authors: Peter Onsomu, James Wainaina, Robert Mbau, Kelvin Muriuki, Paul Machau
For this project, we will use a statistical modelling called multiple linear regression modeling to analyze house sales in a northwestern county. We will work in providing stakeholders with information on how to increase their return on investment and the factors that contribute to get high returns depending on the house features and the house ratings.
The real estate market is a significant sector of the economy, with many homeowners buying and selling properties as investments. As such, it's essential for stakeholders in the real estate industry to maximize their returns on these investments. One way to achieve this is by providing advice to homeowners about how home renovations might increase the estimated value of their homes, and by what amount. This is where multiple linear regression modeling comes into play. In this project, we will use statistical modeling to analyze house sales in a northwestern county. Our goal is to provide insights into the relationship between various home features and the sale price of the houses. By identifying the features that have the most significant impact on the sale price, we can help real estate stakeholders make informed decisions about home renovations and investments. We will be using the King County House Sales dataset for our analysis, which contains information about various home features and the sale price of the houses. We will perform exploratory data analysis, handle missing data and outliers, and assess the assumptions of linear regression before interpreting the results. Our ultimate aim is to help stakeholders in the real estate industry make better-informed decisions that will enable them to fetch more returns on their investments.
The project already had a proposed model, Multiple Linear Regression Model, we will use the preprocessed data to train it. Conclusion: Using KC House Data to predict house prices is a challenging and exciting task. By using data on diffret features and characteristics of a house, we will develop a regression model that can accurately predict house prices. This will be a systematic process that entails steps such as preprocessing, model training and model tuning. The insights that will be gaioned from the model can help buyers and sellers to make well informed decisions to their advantage.