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- W4383109642 abstract "Machine learning has found wide-ranging applications across numerous industries, including speech recognition, product recommendation, and healthcare. The proposed system aims to apply machine learning techniques to the real estate market, which is highly competitive and constantly fluctuating. One of the main challenges in the current system is the inability to predict future market trends, resulting in price increases. Therefore, the goal is to develop an accurate housing cost prediction model that can help customers make decisions based on their budgets and priorities. To achieve this goal, several Machine Learning algorithms are employed, including Linear Regression, Decision Tree Regression, and Random Forest Regression. Research indicates that the Random Forest Regression algorithm provides the highest accuracy in predicting housing costs, with an accuracy level of 87%. By using this model, customers can invest in a property without the need of a broker." @default.
- W4383109642 created "2023-07-05" @default.
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- W4383109642 date "2023-05-09" @default.
- W4383109642 modified "2023-10-16" @default.
- W4383109642 title "Prediction of Real-Time Estate Pricing using Train-Test Splitting Techniques" @default.
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- W4383109642 doi "https://doi.org/10.1109/iciem59379.2023.10167318" @default.
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