The updated script I made to predict housing price predicts based on square footage, number of bedrooms, and number of bathrooms.
This 3-variable model predicts prices (in 100k) of:
| Name | Actual | Predicted | Deal |
|---|---|---|---|
| Church | 3.99 | 3.96 | Fair deal |
| Hudgins | .97 | 1.649 | Good deal |
| Mathews | 3.475 | 3.076 | Bad deal |
| Mobjack | 2.890 | 3.092 | Good deal |
| Moon | 2.500 | 1.578 | Bad deal |
| New Pt. Comfort | 2.290 | 2.667 | Good deal |
To get these predictions, the model takes the following input arrays:
# number of bedrooms:
x1 = np.array([4.0, 3.0, 5.0, 4.0, 2.0, 3.0], dtype = float)
# square footage:
x2 = np.array([3.680, 1.238, 3.051, 3.524, 1.479, 2.840], dtype = float)
# number of bathrooms:
x3 = np.array([4.0, 1.0, 2.0, 2.0, 1.0, 2.0], dtype = float)
## combine the arrays
xs = np.stack([x1, x2, x3], axis = 1)
# price:
ys = np.array([3.990, .970, 3.475, 2.890, 2.500, 2.290], dtype = float)