In this project, we will 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.
The dataset for this project originates from the UCI Machine Learning Repository. The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts.
This project requires Python 3.x version and the following Python libraries installed:
I also reccommend to install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project which also include jupyter notebook to run and execute IPython Notebook.
In a terminal or command window, navigate to the top-level project repo house-sale-price-prediction-of-king-county-USA/ (that contains this README) and run one of the following commands:
ipython notebook boston_housing.ipynb or
jupyter notebook boston_housing.ipynb
This will open the iPython Notebook software and project file in your browser.
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.