Published Paper Details:

This paper focuses on presenting a case study on predicting Real Estate prices using the machine learning technique of multiple linear regression (MLR) model. In this study the dataset of residential properties from the "Boston housing dataset", Which encompasses features such as the location of the house, construction date of the house, proximity to amenities and number of bedrooms was used. This model is implemented in a beginner-friendly way, emphasizing clear understanding of how each feature contributes and affects the house prices in a particular locality. The evaluation of Model's performance was done using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The results obtained from the study demonstrate that even a basic linear regression model can provide reliable estimates of house prices and offer practical insights for potential buyers, sellers, and developers of the locality. This case study highlights the relevance of linear regression as an accessible and reliable tool for real world house price prediction and decision making.