Skip to content

Analyzed and visualized the dataset for the Zillow data competition on Kaggle, applied different regression models to predict errors in Zillow home value prediction, and identified the important features that induce prediction errors to improve the original algorithm.

Notifications You must be signed in to change notification settings

zyaj/Zillow_zestimate_error

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Project: Zillow zestimate error

Install

This project requires Python 3.6 and the following Python libraries installed:

You will also need to have software installed to run and execute a Jupyter Notebook

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.

Code

Main code is provided in the zillow_error_prediction.ipynb notebook file.

Run

In a terminal or command window, navigate to the top-level project directory Zillow_zestimate_error/zillow (that contains this README) and run one of the following commands:

ipython notebook zillow_error_prediction.ipynb

or

jupyter notebook zillow_error_prediction.ipynb

This will open the Jupyter Notebook software and project file in your browser.

Data

Zillow price Kagge competition: https://www.kaggle.com/c/zillow-prize-1

Features

All features are described by the zillow_data_dictionary.xlsx

About

Analyzed and visualized the dataset for the Zillow data competition on Kaggle, applied different regression models to predict errors in Zillow home value prediction, and identified the important features that induce prediction errors to improve the original algorithm.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published