Project: Predicting Boston Housing Prices

Goal: Model Evaluation and Validation for the Prediction of Boston Housing Prices

This report is a modified version of my solution to the 'Boston Housing' Udacity Project that is part of the Machine Learning Engineer Nanodegree program

The report is saved in an iPython Notebook format. To review it click on the Boston_Housing.ipynb file.

If you want to run the code in your computer you will need to follow the Install and Run instructions.

Data

The modified Boston housing dataset consists of 490 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.

Features

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

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

Install

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

• NumPy
• Pandas
• matplotlib
• scikit-learn

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.

Run

In a terminal or command window, navigate to the top-level project directory 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.