Abstract:
In this work, I explore the use of a machine-learning system to forecast whether the
Home will be our asset or Liability after buying it. While there are certainly many
factors to consider while buying a home, the house price is the most important one
for budget. The purpose of this research is to forecast the Assets or liabilities of
people in the middle and lower classes determined by their financial situations. This
research helps real estate people to determine the buying a house provides profit or
losses. Imagine being able to calculate a home's Capital (asset or liability) based on
its year-built sales, neighborhood, square footage, and number of bathroom and
bedroom spaces, House price. And ensure that the House will be a liability or asset
for people. The first phase of the thesis work is gathering a substantial, rigorously
cleansed dataset. Thus, estimating the value of houses profitably and accurately is
the project's main objective. When Determining house Capital, several aspects need
to be taken into account to accurately foresee housing expenses for clients subject
to their goals and budgets. Locale, year-built sales, bathroom, bedroom, and amount
of space and price
