Repository for source code of kaggle competition: House Prices: Advanced Regression Techniques
There are several factors that influence the price a buyer is willing to pay for a house. Some are apparent and obvious and some are not. Nevertheless, a rational approach facilitated by machine learning can be very useful in predicting the house price. A large data set with 79 different features (like living area, number of rooms, location etc) along with their prices are provided for residential homes in Ames, Iowa. The challenge is to learn a relationship between the important features and the price and use it to predict the prices of a new set of houses.
The score is not good at all.But it was my first kaggle submission. :P
Working with and transforming other features in the training set
Experimenting with different modeling techniques, such as Random Forest Regressors or Gradient Boosting