The Boston housing market is highly competitive, and you want to be the best real estate agent in the area. To compete with your peers, you decide to leverage a few basic machine learning concepts to assist you and a client with finding the best selling price for their home. Luckily, you've come across the Boston Housing dataset which contains aggregated data on various features for houses in Greater Boston communities, including the median value of homes for each of those areas. Your task is to build an optimal model based on a statistical analysis with the tools available. This model will then be used to estimate the best selling price for your clients' homes.
Apply basic machine learning concepts on data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home, highlighting basic machine learning practices like splitting data into training and testing and using cross-validation for model selection.
This project requires Python 2.7 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. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
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.
The modified Boston housing dataset consists of 490 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-student ratio by town
Target Variable
4. MEDV
: median value of owner-occupied homes