ML.NET Regression Example which is applied to Boston's Housing Dataset. The input columns are:
- CRIM = Per capita crime rate by town
- ZN = Proportion of residential land zoned for lots over 25,000 sq.ft
- INDUS = The 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)
- RM = The average number of rooms per dwelling
- AGE = The proportion of owner-occupied units built before 1940
- DIS = Weighted distances to five Boston employment centers
- 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
- The output is the price in $1000's.
The whole training data is 80% of total inputs so the rest is used for the test data set. A dummy prediction is done that is presented below.
