Real estate price prognostication through machine learning models

In the dynamic realm of real estate, house prices became the crucial part to encounter the set of hurdles to make lives better. Through meticulous feature engineering, which includes advanced data cleansing and feature manipulation, coupled with robust machine learning techniques, our model offers a nuanced understanding of valuation dynamics. The optimization process involves hyper parameter tuning and cross-validation, employing cutting-edge methodologies to extract latent patterns and yield meaningful insights from the underlying data. Leveraging algorithms of supervised learning algorithms like linear regression and K-fold, chosen for their ability to discern intricate patterns within diverse datasets, our research pioneers a transformative approach to real estate valuation. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE) and R-squared were used to ensure a robust and accurate predictive framework which were promising