A prediction model using decision trees
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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