Machine Learning Course

Supervised Learning

Project: Boston Housing

Install

This project requires Python 3.x and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn
• seaborn

You will also need to have software installed to run and execute an iPython Notebook

I recommend installion Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.

Code

Template code is provided in the Number_of_Rooms_Predicition.ipynb notebook file.

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 Number_of_Rooms_Predicition.ipynb

or

jupyter notebook Number_of_Rooms_Predicition.ipynb

This will open the iPython Notebook software and tutorial file in your browser.

Data

The dataset originally came from here: https://github.com/selva86/datasets/blob/master/BostonHousing.csv

Origin: The origin of the boston housing data is Natural.

Number of Cases: The dataset contains a total of 506 cases.

Features

• CRIM : per capita crime rate by town.
• ZN : proportion of residential land zoned for lots over 25,000 sq.ft.
• INDUS : proportion of non-retail business acres per town.
• CHAS : Charles River dummy variable (1 if tract bounds river; 0 otherwise).
• NOX : nitric oxides concentration (parts per 10 million).
• AGE : proportion of owner-occupied units built prior to 1940.
• DIS : weighted distances to five Boston employment centres.
• RAD : index of accessibility to radial highways.
• TAX : full-value property-tax rate per $10000.
• PTRATIO : pupil-teacher ratio by town.
• B : 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.
• LSTAT : % lower status of the population.
• MEDV : Median value of owner-occupied homes in $1000's.

Target Variable

• RM : average number of rooms per dwelling.