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Machine Learning Engineer Nanodegree

Model Evaluation and Validation

Project: Predicting Boston Housing Prices


This project requires Python 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.


Template code is provided in the boston_housing.ipynb notebook file. You will also be required to use the included 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. Note that the code included in is meant to be used out-of-the-box and not intended for students to manipulate. 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


jupyter notebook boston_housing.ipynb

This will open the Jupyter Notebook software and project file in your browser.


The modified Boston housing dataset consists of 489 data points, with each datapoint having 3 features. This dataset is a modified version of the Boston Housing dataset found on the UCI Machine Learning Repository.


  1. RM: average number of rooms per dwelling
  2. LSTAT: percentage of population considered lower status
  3. PTRATIO: pupil-teacher ratio by town

Target Variable 4. MEDV: median value of owner-occupied homes

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