jrbeverly / boston-housing

Leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home.

Predicting Boston Housing Prices

Evaluate 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.

Getting Started

You can spin up the environment using docker by running the following commands:

bash docker.bash

# inside docker container
bash .build/conda.bash

Alternatively you can manually install Python 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

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