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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.

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yangvnks/housing-regression

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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

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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.

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