Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 

README.md

Predicting Boston Housing Prices

Model Evaluation & Validation Project

A prediction model using a decision tree to determine what an optimal price might be for a house, based on historic housing data from Boston. This project demonstrates the efficacy of using GridSearch algorithm to find optimal parameters for a learning model.

Install

This project requires Python 2.7 and the following Python libraries installed:

In addition, you will need to be able to run an jupyter Notebook to go along with this project.

Run

In a terminal/command window, go to the top-level project directory boston_housing/ (that contains this README). Then run:

jupyter notebook boston_housing.ipynb

Data

The dataset used in this project is included with the scikit-learn library (sklearn.datasets.load_boston). You do not have to download it separately.

It contains the following attributes for each housing area, including median value (which you will try to predict):

  • CRIM: per capita crime rate by town
  • ZN: proportion of residential land zoned for lots over 25,000 sq.ft.
  • INDUS: proportion of non-retail business acres per town
  • CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
  • NOX: nitric oxides concentration (parts per 10 million)
  • RM: average number of rooms per dwelling
  • AGE: proportion of owner-occupied units built prior to 1940
  • DIS: weighted distances to five Boston employment centres
  • RAD: index of accessibility to radial highways
  • TAX: full-value property-tax rate per $10,000
  • PTRATIO: pupil-teacher ratio by town
  • B: 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
  • LSTAT: % lower status of the population
  • MEDV: Median value of owner-occupied homes in $1000's

About

A prediction model using decision trees

Resources

Releases

No releases published

Packages

No packages published
You can’t perform that action at this time.