This project leverages a comprehensive dataset encompassing features such as area, number of bedrooms and bathrooms, stories, and amenities including a guest room, basement, hot water heating, air conditioning, parking spaces, preferred location, and furnishing status. The primary objective is to develop and evaluate a machine learning model capable of accurately predicting house prices.
In this project, linear regression was specifically employed to analyze the relationships between the various features and the target variable—house price. Linear regression assumes a linear relationship between the dependent variable (price) and independent variables (features), allowing for effective modeling of the data.