Abstract:
Predictive models for determining the “Predicting House Price Using Cat Boost Based machine learning Model” remaining as more challenging and tricky task. The sale price of properties in cities depends on a number of interdependent factors. Key factors that might affect the price include area of the property, location of the property and its amenities. In this Project, an analytical study has been carried out by considering the data set that remains open to the public by illustrating the available housing properties in machine hackathon platform. The data set has eighty-one features. In this study, an attempt has been made to construct a predictive model for evaluating the price based on the factors that affect the price. Modeling explorations apply some regression techniques such as multiple linear regression (Least Squares), Lasso and Ridge regression models, support vector regression, and boosting algorithms such as Extreme Gradient Boost Regression (XG Boost), Cat Boost. Such models are used to build a predictive model, and to pick the best performing model by performing a comparative analysis on the predictive errors obtained between these models. Here, the attempt is to construct a model for evaluating the price based on factors that affects the price.
