Introducing "HomeValue Pro": Your personalized house price prediction model powered by cutting-edge data analysis and linear regression algorithms.
HomeValue Pro harnesses the wealth of historical housing data, including factors such as location, square footage, number of bedrooms and bathrooms, amenities, and neighborhood characteristics. Through meticulous data analysis, our model uncovers hidden patterns and correlations to accurately predict property values.
Using linear regression, HomeValue Pro calculates the optimal coefficients for each feature, allowing for precise estimation of house prices based on input variables. By leveraging the linear relationship between independent variables and house prices, our model delivers actionable insights to homeowners, buyers, and real estate professionals alike.
Whether you're looking to sell, buy, or invest in real estate, trust HomeValue Pro to provide you with reliable price predictions, empowering you to make informed decisions in today's dynamic housing market. Welcome to a smarter way of navigating the world of real estate with HomeValue Pro.
- Creative feature engineering
- Advanced regression techniques like random forest and gradient boosting
The goal is to predict the sales price for each house. For each Id in the test set, the model will predict the value of the SalePrice variable.
Submissions are evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses and cheap houses will affect the result equally.)
- Python
- Jupyter Notebooks