House Prices - Advanced Regression Techniques

Introduction to the challange

In the House Prices - Advanced Regression Techniques project, I aimed to develop a machine learning model that could accurately predict the sale prices of houses in Ames, Iowa, based on a variety of features. The dataset used in this project contains 79 explanatory variables describing (almost) every aspect of residential homes in Ames. With this large and complex dataset, I employed a variety of feature engineering techniques, data preprocessing methods, and machine learning algorithms to build a robust and accurate model. By successfully predicting the sale prices of houses, this project has practical implications for real estate companies and individuals looking to buy or sell homes.

Key insights

After conducting a thorough analysis of the dataset, we have identified several actionable insights that can help improve the overall value of a property.

Our analysis shows that the number of bathrooms in a property is more critical than the number of bedrooms when it comes to determining its value, with a correlation coefficient of 0.42 between the number of total bathrooms and sale price. This indicates that having an adequate number of bathrooms can greatly improve a home's functionality and livability and increase its value. This is likely due to the fact that people value privacy and convenience when it comes to bathrooms, and having an adequate number of bathrooms can greatly improve a home's functionality and livability. Additionally, the correlation coefficient of 0.28 between bath per bedroom and sale price suggests that the ratio of bathrooms to bedrooms is not as essential to consider when evaluating a property. In contrast, the number of bedrooms may not be as critical, as some people may prefer larger bedrooms or use them for other purposes such as a home office or gym.

Another key insight that our analysis has revealed is that having a pool does not appear to be a significant factor in determining the value of a property, with a correlation coefficient of only 0.07 between having a pool and sale price. Instead, adding a garage or porch appears to be more critical, with a correlation coefficient of 0.2 for having a porch and 0.17 for having a garage. This indicates that the additional space and functionality provided by a garage or porch can have a more significant impact on the value of a property than a pool, which may require additional maintenance and upkeep, as well as the limited usability in some climates. In contrast, a garage provides a convenient and secure location to store vehicles, while a porch provides additional space for outdoor living and entertaining.

Finally, our analysis suggests that having a porch can greatly impact the value of a property, regardless of its size, with a correlation coefficient of 0.2 between having a porch and sale price, and a correlation coefficient of 0.13 between total porch area and sale price. This suggests that even a small porch can provide significant value to a home. This is because a porch provides a space where people can relax and enjoy the outdoors without having to leave the comfort of their home. In contrast, the size of the porch does not have as much of an impact on the value of the property.