In places like Bangalore, developing predictive models for home selling prices is still a difficult and time-consuming job. The price at which houses in major cities are being sold as in bangalore is reliant on a variety of interconnected variables. The area of the house where you live may have a significant impact on the pricing. Property, including the physical location as well as the features and services on the property. Due to the fact that there has been research done and an analysis done by taking into account the data set that is still available to the general public using a machine to depict the various housing options platform for hack athons there are nine different variables in this collection of information. According to the findings of this investigation, attempts have been made to develop a model that can predict assessing the pricing in light of the influencing variables. Some regression methods, such as are used in modeling experiments, such the least squares method of multivariate linear regression, the lasso, and the ridge models of regression, support vector regression, and methods for enhancing such as extreme gradient boost regression (xg) methods; boost). These models are used in the construction of prediction models and in the analysis of decide on the most effective design by doing a comparison an examination of the disparity in prediction error between the two models. It is hoped that by doing this, a predictive model may be built for assessing the pricing in light of the influencing variables.

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