This project concerns the Boston House Prices dataset, which was first published in 1978 contains US census data concerning houses in various areas around the city of Boston. The project consists in descriptive and inferential statistics, and prediction of the variable price using keras to create a neural network.

This project requires Python 3.7 and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• seaborn
• scipy
• sklearn
• statsmodel
• tensorflow
• keras

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_House_Dataset.ipynb notebook file.

The file can also be viewed at [https://nbviewer.jupyter.org/github/npradaschnor/machine-learning-project/blob/master/Boston_House_Dataset.ipynb]