Written By Ryan Boldi, Abhishek Rao Chimbili and Abdulla Alattar

The files for this program are MLR.py. It uses sklearn's boston dataset to predict house prices based on a bunch of independant variables.

There are two different classifiers that I tried to use, a support vector machine, and a simple linear regression model. The linear regression model had much higher confidence and accuracy than the SVM, so I based all the predictions off of the linear regression model.

Boston Dataset:

Data Set Characteristics:

Number of Instances: 506

Number of Attributes: 13 numeric/categorical predictive

Median Value (attribute 14) is the target for this project

Attribute Information (in order):

• 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 rive r; 0 otherwise)
• NOX nitric oxides concentration (parts per 10 million)
• RM average number of rooms per dwelling
• 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 $10,000
• 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

Creator: Harrison, D. and Rubinfeld, D.L.

Can predict MEDV if given the 13 Independant variables