The dataset describes 13 numerical properties of houses in Boston suburbs, and is concerned with modeling the price of houses in those suburbs in thousands of dollars. As such, this is a regression predictive modeling problem. Input attributes include things like crime rate, proportion of non-retail business acres, chemical concentrations, and more. In the following list are shown all the variables followed by a brief description: 

• Number of instances: 506
• Number of attributes: 13 continuous attributes (including class
  attribute MEDV), and one binary-valued attribute

Each of the attributes is detailed as follows:

1. crim per capita crime rate by town.
2. zn proportion of residential land zoned for lots over 25,000 square feet.
3. indus proportion of non-retail business acres per town.
4. chas Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).

5. nox nitric oxides concentration (parts per 10 million).
6. rm average number of rooms per dwelling.
7. age proportion of owner-occupied units built prior to 1940.
8. dis weighted distances to five Boston employment centres
9. rad index of accessibility to radial highways.
10. tax full-value property-tax rate per $10,000.
11. ptratio pupil-teacher ratio by town.
12. black 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.
13. lstat percent lower status of the population.
14. medv median value of owner-occupied homes in $1000's.

Of these, medv is the response variable, while the other thirteen variables are possible predictors. The goal of this analysis is to fit a regression model that best explains the variation in medv.

There is a relation between the first thirteen columns and the medv response variable. We can predict the medv value based on the input thirteen columns.