predicting house prices using multiple variables linear regression

In the pursuit of advancing real estate price prediction models, this study focuses on the application of multiple linear regression techniques to forecast house prices. The model is meticulously crafted, utilizing a select set of features known to have a significant impact on property valuation. These features include “OverallQual,” representing the overall material and finish quality, “GrLivArea,” the above-ground living area square footage, “GarageCars,” the size of the garage in car capacity, “GarageArea,” the size of the garage in square footage, and “TotalBsmtSF,” denoting the total square footage of the basement area.

To enhance the predictive performance and ensure the integrity of the regression analysis, each feature underwent a normalization process. This step is crucial to align the scales of the variables, thereby facilitating a more accurate and interpretable model. The normalization process also aids in mitigating the influence of outliers, ensuring that each feature contributes proportionately to the final prediction.

The implementation of this refined approach to feature selection and normalization is anticipated to yield a robust and reliable model, capable of providing valuable insights into the dynamics of real estate pricing. The findings of this study are expected to serve as a cornerstone for future research in the domain of property price prediction, leveraging the power of linear regression analysis.