valuation of house prices using predictive techniques
₹400-750 INR / hora
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.
Nº del proyecto: #17722403
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23 freelancers están ofertando un promedio de ₹749 / hora por este trabajo
Hi I am a very experienced statistician, data scientist and academic writer. I have completed several PhD level thesis projects involving advanced statistical analysis of data. I have worked with data from several comp Más
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I have done a course on Business Analytics and have done similar kind of projects during the study. Hence should be able to pick this easily.
have working experience in c++ and c for more than 8 years with various mncs , executed couple of projects from scratch and delivered successfully.
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Willing to commit for the project Relevant Skills and Experience Worked on similar data in hackathon.
I am proposing this project that, i will give 100% Data Science solution with in time using R and Python by applying Machine Learning, Deep Learning algorithms.
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