Project: Predicting Boston Housing Prices
Introduction
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.
Install
This project requires Python and the following Python libraries installed:
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.
Run
In a terminal or command window, navigate to the top-level project directory boston_housing/
(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 Jupyter Notebook software and project file in your browser.
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 town
Target Variable
4. MEDV
: median value of owner-occupied homes
NOTE:- If the code does not work, create a new folder called "pycache" in workspace and move visuals.cpython-36.pyc in it.