House price prediction means guessing how much a house will be valued by looking at things like where, how big it is, and what extra features it has. It's important because it helps buyers, sellers, investors, and banks make smart choices. This ensures fair prices, good investment plans, and correct loan evaluations. Linear regression is a way to understand how one thing affects another by using a straight-line equation. It looks at the connection between a main factor (the dependent variable) and one or more other factors (the independent variables). It helps predict what will happen in the future, see how different things are related, and find patterns based on past information. Categorical data encoding methods change categories that don't have numbers into numbers so they can be used in machine learning models. One common way to handle categorical data is One-Hot Encoding. This study aims to enhance the ability to forecast house prices through the application of linear regression and more effective methods for managing various types of data to improve how accurate the model is, to look at different ways to encode data, and to check how well it works to give better predictions of property values. In the future, this study strategy to look into better machine learning methods, add more factors like economic data, and improve how to categorize information. These changes aim to make predictions about house prices more accurate and better fit the changing real estate market.