Machıne Learnıng For Home Value Predıctıon

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Mayank Kumar Singh, Arvind Kumar Singh, Priyanshu Singh


The real estate industry is the minimal pellucid in our surroundings. Home prices vary on a regular basis and are sometimes blown up rather than based on evaluation. Our study project's major focus is on predicting house values using real-world factors. Our goal is to base our assessments on each and every key parameter that is taken into account when establishing the pricing. We used multiple linear regression technique to determine property prices based on square footage and the number of rooms in this paper. The relationship between the average value of one variable and the values of other variables is calculated through regression. Regression is a type of statistical methods for estimating the relationships between variables in statistical modelling. The relationship between one dependent variable (y) and two or more independent variables (x1, x2, etc.) is explained by multiple linear regression Three modules were used to implement this: The data entry module is used to give the project with the information it requires. The Analysis module is used to examine and forecast housing prices based on the demands of the customer. The Front-end component has been used to construct the  blueprint required GUI screens.

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