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@article{159827,
        author = {HARSHADA SUNIL BELSARE and Prof. K. V. Warkar},
        title = {A Model for House Price Prediction Using Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {12},
        pages = {1040-1047},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159827},
        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.},
        keywords = {Data standardisation, home price forecasting, machine learning, and machine learning algorithms are some of the keywords},
        month = {},
        }