A Model for House Price Prediction Using Machine Learning Algorithms
Author(s):
HARSHADA SUNIL BELSARE, Prof. K. V. Warkar
Keywords:
Data standardisation, home price forecasting, machine learning, and machine learning algorithms are some of the keywords
Abstract
In this, we will anticipate housing prices using machine learning techniques. Every year, there is a significant growth in the price of homes, thus we felt the need for a system that can forecast future home prices. People cannot accurately estimate the price of homes due to a lack of understanding about real estate assets. As a result, we realised the need for a model that can accurately forecast home prices. Therefore, the primary goal of our research is to accurately and profitably forecast the price of a property. The outcomes of the algorithms utilised are compared in this survey, and the model with the best accuracy and lowest error rate will be adopted. When selecting a prediction method, We frequently evaluate and compare a variety of prediction methods while choosing a prediction method. Due to its reliable and probabilistic model selection process, we frequently use the linear, and random forest regression models. Our findings demonstrate that the problem-solving strategy should be successful and flexible enough to produce forecasts that can be compared to other models for predicting housing prices. We frequently suggest a house price prediction model to help a consumer determine the accurate value of a home.
Article Details
Unique Paper ID: 159827

Publication Volume & Issue: Volume 9, Issue 12

Page(s): 1040 - 1047
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