• Divyanshu Sharma
• J.JAI INDRA REDDY

The correct estimation of real estate prices plays an important role in many areas such as housing, finance and urban planning. Machine learning algorithms are gaining traction for their ability to analyze complex patterns in real estate data and provide reliable predictions. In this study, we propose an effective method to predict house prices using machine learning techniques. First we collected different information such as location, size, number of bedrooms, number of bathrooms and other related items. Next, we preprocess the data by addressing missing values, encoding categorical variables, and normalizing numeric properties. To improve performance, we provide engineering techniques such as dimensionality reduction and feature selection to extract useful insights from data. We use many machine learning algorithms, including linear regression, tree pruning, random forests, support vector regression, and gradient boosting. These algorithms are trained on a subset of the data and applied using appropriate metrics to evaluate their performance. Performance measures such as square of error, mean standard error, and R-square are used to measure the accuracy and reliability of the prediction model. Experimental results show that our combined approach gives good results in accurately estimating house prices. Overall, our research demonstrates the ability of machine learning to accurately predict home prices. Real estate professionals, investors and policy makers can use the planning process to make decisions, develop pricing strategies and contribute to business development.

"Unveiling the Power of Machine Learning in House Price Prediction: Algorithm Selection and Evaluation Strategies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 8, page no.d158-d164, August-2023, Available :http://www.jetir.org/papers/JETIR2308321.pdf

"Unveiling the Power of Machine Learning in House Price Prediction: Algorithm Selection and Evaluation Strategies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 8, page no. ppd158-d164, August-2023, Available at : http://www.jetir.org/papers/JETIR2308321.pdf