%0 Journal Article %1 noauthororeditor %A D, Jakir Khan | Dr. Ganesh %D 2021 %J International Journal of Trend in Scientific Research and Development %K Learning Linear Machine Regression algorithm %N 4 %P 795-799 %T House Price Estimates Based on Machine Learning Algorithm %V 5 %X Housing prices are increasing every year, necessitating the creation of a long term housing price strategy. Predicting a homes price will assist a developer in determining a homes purchase price, as well as a consumer in determining the best time to buy a home. The sale price of real estate in major cities depends on the specific circumstances. Housing prices are constantly changing from day to day and are sometimes fired rather than based on estimates. Predicting real estate prices by real factors is a key element as part of our analysis. We want to make our test dependent on all of the simple metrics that are taken into account when deciding the significance. In this research we use linear regression techniques pathway and our results are not self inflicted process rather is a weighted method of various techniques to give the most accurate results. There are fifteen features in the data collection. In this research. There has been an effort to build a forecasting model for determining the price based on the variables that influence the price.The results have proven to be effective lower error and higher accuracy than individual algorithms are used. Jakir Khan | Dr. Ganesh D "House Price Estimates Based on Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42367.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42367/house-price-estimates-based-on-machine-learning-algorithm/jakir-khan

@article{noauthororeditor, abstract = {Housing prices are increasing every year, necessitating the creation of a long term housing price strategy. Predicting a homes price will assist a developer in determining a homes purchase price, as well as a consumer in determining the best time to buy a home. The sale price of real estate in major cities depends on the specific circumstances. Housing prices are constantly changing from day to day and are sometimes fired rather than based on estimates. Predicting real estate prices by real factors is a key element as part of our analysis. We want to make our test dependent on all of the simple metrics that are taken into account when deciding the significance. In this research we use linear regression techniques pathway and our results are not self inflicted process rather is a weighted method of various techniques to give the most accurate results. There are fifteen features in the data collection. In this research. There has been an effort to build a forecasting model for determining the price based on the variables that influence the price.The results have proven to be effective lower error and higher accuracy than individual algorithms are used. Jakir Khan | Dr. Ganesh D "House Price Estimates Based on Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42367.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42367/house-price-estimates-based-on-machine-learning-algorithm/jakir-khan }, added-at = {2021-07-15T10:21:00.000+0200}, author = {D, Jakir Khan | Dr. Ganesh}, biburl = {https://www.bibsonomy.org/bibtex/23e854477c23ee1d0adc4d2a5d15a938f/ijtsrd}, interhash = {03b3741dd1df6280f27f5ee9eb1ceba0}, intrahash = {3e854477c23ee1d0adc4d2a5d15a938f}, issn = {2456-6470}, journal = {International Journal of Trend in Scientific Research and Development}, keywords = {Learning Linear Machine Regression algorithm}, language = {english}, month = jun, number = 4, pages = {795-799}, timestamp = {2021-07-15T10:21:00.000+0200}, title = {House Price Estimates Based on Machine Learning Algorithm }, volume = 5, year = 2021 }