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The goal of this file is to normalize raw car attributes for the prediction and transform the price using the inverse
of the standardize process.
import numpy as np
class Predict:
We save the data set metadata.
def __init__(self, mean_km, std_km, mean_age, std_age, min_price, max_price):
self.mean_km = mean_km
self.std_km = std_km
self.mean_age = mean_age
self.std_age = std_age
self.min_price = min_price
self.max_price = max_price
This method returns the car's data normalized using the data set metadata.
def input(self, km, fuel, age):
km = (km - self.mean_km) / self.std_km
fuel = -1 if fuel == 'Diesel' else 1
age = (age - self.mean_age) / self.std_age
return np.matrix([[
km, fuel, age
This method returns the price in euro from the output of the network. The inverse of the standardize process for the
price is applied.
def output(self, price):
return price * (self.max_price - self.min_price) + self.min_price