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.
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:
You will also need to have software installed to run and execute a Jupyter Notebook
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.
RM: average number of rooms per dwelling
LSTAT: percentage of population considered lower status
PTRATIO: pupil-teacher ratio by town
MEDV: median value of owner-occupied homes (Target Variable)