The objective of this project is to devise a machine learning model that can proficiently forecast the sale prices of residential properties based on an array of features such as geographical location, dimensions, age, and state of upkeep. The absence of a dependable and accurate house price prediction system presents a substantial predicament for all parties involved in real estate transactions, and may result in adverse consequences such as undervaluation or overvaluation of properties. Hence, the successful development and deployment of a reliable house price prediction model would not only yield insightful outcomes for property transactions, but also enhance the effectiveness and transparency of the real estate sector. Within this undertaking, a range of regression methodologies shall be explored in order to address such a compelling conundrum.