This project contains the following files:
boston_housing.ipynb
: Jupyter notebook containing project code.housing.csv
: The project dataset. You'll load this data in the notebook.visuals.py
: This Python script provides supplementary visualizations for the project.boston_housing.ipynb
: project notebook (Jupyter - Python 3.5)report.html
: A HTML export of the project notebookproject_discription.md
: been provided with the project files which may contain additional necessary information or instruction for the project.project_discription.md
: Predicting Boston Housing Prices project rubric.
This project requires Python 2.7 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.
This dataset is a modified version of the Boston Housing dataset found on the UCI Machine Learning Repository. It consists of 489 data points, with each datapoint having 3 features.
Features
RM
: average number of rooms per dwellingLSTAT
: percentage of population considered lower statusPTRATIO
: pupil-teacher ratio by town
Target Variable
MEDV
: median value of owner-occupied homes
Code is provided in the boston_housing.ipynb
notebook file.
It requires the included visuals.py
Python file and the housing.csv
dataset file to work.
Note that the code included in visuals.py
is meant to be used
out-of-the-box. If you are interested in how the visualizations are
created in the notebook, please feel free to explore this Python file.
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
or
jupyter notebook boston_housing.ipynb
This will open the Jupyter Notebook software and project file in your browser.