This is my capstone project for my subject at HUST: Introduction to Artificial Intelligence.
House price prediction is an important concept in the real estate industry and has been a popular problem in research for years since the traditional house price prediction depend on cost and sale price comparison does not satisfy the accepted standards and certification process. In addition to getting accurate prediction it is important to know the factors that have a significant impact on the house price. In this project on House Price Prediction, our task is to predict house prices in Boston using different approaches.
In the EDA folder, we do the exploratory data analysis to exploit the features of the data.
After that, we applied different methods to solve the prediction problems:
- Linear Regression
- Neural Network method
- Tree-based methods
For more details, you can see our documents in docs folder, which have proposal file, report file and slides.