In this paper, we are predicting the sale price of the houses using various machine learning algorithms. Housing sales price are determined by numerous factors such as area of the property, location of the house, material used for construction, age of the property, number of bedrooms and garages and so on. This paper uses machine learning algorithms to build the prediction model for houses. Here, machine learning algorithms such as logistic regression and support vector regression, Lasso Regression technique and Decision Tree are employed to build a predictive model. We have considered housing data of 3000 properties. Logistic Regression, SVM, Lasso Regression and Decision Tree show the R-squared value of 0.98, 0.96,0.81 and 0.99 respectively. Further, we have compared these algorithms based on parameters such as MAE, MSE, RMSE and accuracy. This paper also represents significance of our approach and the methodology.
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Hello there, I am an analytics consultant with more than 6 years of experience working with clients across the globe on data driven decision making. Please ping for further details. Best, Yash
have working experience in c++ and c for more than 8 years with various mncs , executed couple of projects from scratch and delivered successfully.