udacity-model-evaluation-and-validation

Predicting Boston Housing Prices

Requirements

• Language:- Python 2.7

  Python libraries:

  • NumPy
  • Pandas
  • matplotlib
  • scikit-learn (v0.17)

  Additional software:

  • Jupyter Notebook to run IPython notebooks.

Code

The original template and supporting files are taken from projects/boston_housing folder of Udacity's machine-learning repository on GitHub.

Review: Model evaluation and validation rubric

Run

In a terminal or command window, navigate to the folder boston_housing/ in project directory and run one of the following commands:

jupyter notebook boston_housing.ipynb

or

ipython notebook boston_housing.ipynb

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

Data

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.

Features

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