The dataset for this project originates from the UCI Machine Learning Repository. The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts.
This project requires Python 2.7 and the following Python libraries installed:
You will also need to have software installed to run and execute an iPython Notebook
Template code is provided in the boston_housing.ipynb notebook file. You will also be required to use the included visuals.py Python file and the housing.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project.
In a terminal or command window, navigate to the top-level project directory boston_housing/ (that contains this README) and run one of the following commands:
ipython notebook boston_housing.ipynb
jupyter notebook boston_housing.ipynb
This will open the iPython Notebook software and project file in your browser.
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. You can find more information on this dataset from the UCI Machine Learning Repository page.