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.
- Yannick Giovanakis (@yangvnks)
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 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 |
\contains all of the jupyter's notebooks including classifiers, preprocessing and data visualization\Datacontains the project dataset given in the Kaggle challenge\Data\outputscontains the outputs given by the classifiers that were submitted to Kaggle
To run the jupyter's notebooks just go with jupyter notebook