Use numpy to write a neural network to complete the Boston house price prediction
- l1 = Linear (X, W1, b1)
- s1 = Relu (l1)
- l2 = Linear (s1, W2, b2)
- cost = MSE (y, l2)
- Hidden layer dimension is 10
| Variables | Defination |
|---|---|
| CRIM | Crime rate per capita |
| ZN | Proportion of resident land |
| INDUS | Proportion of non-retail commercial land |
| CHAS | 0 or 1 |
| NOX | Nitric oxide concentration |
| RM | Average number of rooms per house |
| AGE | Proportion of self-use houses before 1940 |
| DIS | Weighted distance from five Boston CBD |
| RAD | Convenience index from highway |
| tax | Real estate tax rate per 10,000 US dollars in the region |
| PETATIO | Teacher-student ratio in the area |
| B | Proportion of black people in the region |
| LSTAT | Proportion of low- and middle-income groups in the region |