Machine learning report

Ištrauka

Introduction
The motivation for this project is to address the critical issue of housing
affordability and homelessness in California. Using the 1990 census data, the
aim is to provide policymakers, real estate agencies, and homebuyers with
actionable insights and recommendations. This predictive modeling project
seeks to enhance decision-making and affordability understanding.
The dataset for this project focuses on housing in California, using data from the 1990 census. It
includes a variety of features such as median housing price, income levels, geographical location,
and population density. Understanding the housing market is crucial for several stakeholders,
such as policymakers, real estate agents, and buyers, as it helps in pricing decisions and
analyzing market trends.
The motivation for this project is rooted in the complexity of the housing market. Housing prices
are affected by a mix of social, economic, and geographic factors, which interact in complicated
ways. While advanced predictive models exist, they often lack interpretability, leaving many
questions about what drives housing prices unanswered.