House prices : advanced regression challenge
Housing price regression challenge on Kaggle. Given a dataset of a subset of the house with known prices predict new house prices based on a set of features.
Credits
- Yannick Giovanakis (@yangvnks)
Method
Below are provided the steps that were followed for this project. Each step and classifiers have their own document.
- Data visualisation & Preprocessing: with the knowledge acquired with the preceding step, apply preprocessing of data including dealing with missing values, drop unuseful features and build new features
- Regression: use regression techniques based on the preprocessed data using a variety of algorithms
Regression techniques
Regression techniques together with the relative scores (RMSE)
Regressor | CV score | Kaggle score |
---|---|---|
ENet | 0.10811 | 0.11926 |
GBoost | 0.10882 | 0.12412 |
XGB | 0.11041 | 0.12188 |
KRR | 0.11202 | - |
Ensemble | 0.1051 | 0.11765 |
Folder structures
\
contains all of the jupyter's notebooks including classifiers, preprocessing and data visualization\Data
contains the project dataset given in the Kaggle challenge\Data\outputs
contains the outputs given by the classifiers that were submitted to Kaggle
To run the jupyter's notebooks just go with jupyter notebook