/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.

Primary LanguageJupyter Notebook

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

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