| Leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home. |  |
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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:
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
RM: average number of rooms per dwellingLSTAT: percentage of population considered lower statusPTRATIO: pupil-teacher ratio by townMEDV: median value of owner-occupied homes (Target Variable)