House Capital Prediction Using Machine Learning in Python

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