# Boston Housing Prices

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.

## Dependencies

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

-- sklearn
-- jupyter notebook


### Installing

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.

## Usage

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

### Known Issues

None at this time.

### Future Planes

None at this time

### Contribution guidelines

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!

### License

Project is open sourced under the Apache 2.0 license.