Price Prediction of House using KNN based Lasso and Ridge Model

Getting a house of our wishes within our budget in a residential area of our customization is quite a tedious process. In order to overcome this, we have developed a model to get a house of our interest with religious belief and budget this Implemented model is of linear regression and k nearest nei...

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Bibliographic Details
Published in2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) pp. 1520 - 1527
Main Authors R H, Nivitha Shree, R, Poojaa, R, Rithick Roshan, P, Mohan Kumar
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.04.2022
Subjects
Data communication
Data models
House Price Prediction
KNN
Lasso
Linear regression
Prediction algorithms
Predictive models
Ridge
Training
Urban areas
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DOI10.1109/ICSCDS53736.2022.9760832

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Summary:Getting a house of our wishes within our budget in a residential area of our customization is quite a tedious process. In order to overcome this, we have developed a model to get a house of our interest with religious belief and budget this Implemented model is of linear regression and k nearest neighbor's algorithm with gradient descent optimization to make an optimal model for predicting house prices using the dataset. Performed feature engineering and selection using lasso and ridge penalties to eliminate features that had little or no impact on the residual sum of squares error. Then exposes Jupyter notebook cells as REST Endpoints to make predictions with new information. Finally, we are trying to send an email alert to the concerned user to give an alert of the house price
DOI:10.1109/ICSCDS53736.2022.9760832