TensorFlow 1.x Deep Learning Cookbook
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Getting ready

We will consider the Boston housing price dataset (http://lib.stat.cmu.edu/datasets/boston) collected by Harrison and Rubinfield in 1978. The dataset contains 506 sample cases. Each house is assigned 14 attributes:

  • 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 oxide 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: percent lower status of the population
  • MEDV: Median value of owner-occupied homes in $1,000's