yangvnks / housing-regression

This competition challenges you to predict the final price of each home with 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa.

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.

1. 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
2. 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