Boston House Price Dataset Regression

The problem that we will look at in this project is the Boston house price dataset. The dataset describes 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. There are 13 input variables that describe the properties of a given Boston suburb. The full list of attributes in this dataset are as follows:

1. CRIM: per capita crime rate by town.
2. ZN: proportion of residential land zoned for lots over 25,000 sq.ft.
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 centers.
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. B: 1000(Bk " 0.63)2 where Bk is the proportion of blacks by town.
13. LSTAT: % lower status of the population
14. MEDV: Median value of owner-occupied homes in 1000s.

tutorial source: Deep Learning With Python, Develop Deep Learning Models On Theano And TensorFlow Using Keras, Jason Brownlee