Evaluates the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts. A model trained on this data that is seen as a good fit could then be used to make certain predictions about a home — in particular, its monetary value. This model would prove to be invaluable for someone like a real estate agent who could make use of such information on a daily basis.

The following dependencies will be needed for the program. You can install the dependencies using Python's pip program.

-- sklearn -- jupyter notebook

A step by step series of examples that tell you how to get a development env running

Verify sklearn and jupyter notebook are installed.

Go to the directory of the program.

Type 'jupyter notebook' on the command line for the given directory.

Make sure 'Python 3' is selected.

Run line by line.

After running each line, you'll see outputs below each line whether from status checks or output of regression equations.

Simply download the program by zip file. As long as all libraries are installed, you should be good to go.

None at this time.

None at this time

Please refer to each project's style guidelines and guidelines for submitting patches and additions. In general, we follow the "fork-and-pull" Git workflow.

Fork the repo on GitHub Clone the project to your own machine Commit changes to your own branch Push your work back up to your fork Submit a Pull request so that we can review your changes NOTE: Be sure to merge the latest from "upstream" before making a pull request!

Project is open sourced under the Apache 2.0 license.