A Linear Regression model that predicts the price of the diamond.
The model is trained on a dataset containing the attributes of almost 54,000 diamonds.
- loss: 364475
- mae: 328
- mse: 364475
- price: price in US dollars ($326--$18,823)
- carat: weight of the diamond (0.2--5.01)\n
- cut: quality of the cut (Fair, Good, Very Good, Premium, Ideal)
- color: diamond colour, from J (worst) to D (best)
- clarity: a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))
- x: length in mm (0--10.74)
- y: width in mm (0--58.9)
- z: depth in mm (0--31.8)
- depth: total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)
- table: width of top of diamond relative to widest point (43--95)
The Diamonds.ipynb has a detailed explaination and code for exploratory data analysis and model creation using Tensorflow.
You should first install all the dependency libraries by running the following command
pip install -r requirements.txt
from predict import predictPrice
# Example
predictPrice(carat=2.29, cut='Premium', color='I', clarity='VS2', x=8.5, y=8.47, z=5.16)
# returns float value of price