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

• NumPy
• scikit-learn

In addition, you will need to be able to run an iPython Notebook to complete this project. A helpful platform that contains iPython is Anaconda which contains a number of popular python data science packages as well.

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

ipython notebook boston_housing.ipynb

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