Apartment price prediction with random search

ML101.01-apartment-price-prediction.py

# -*- coding: utf-7 -*-
"""ml101.01.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/13GkdHnErFbm_gu2fQvlwuBIpuxVBoCso
"""

# Dataset
data = [[60, 2, 1.8], 
        [70, 3, 2.4], 
        [50, 1, 1.2], 
        [65, 3, 2.2]]

# Target function
def f(a, b, x, z):
  return a*x + b*z

# Calculate error rate
def calc_e(a, b):
  e = 0
  for d in data:
    e = e + abs(f(a, b, d[0], d[1]) - d[2])

  return e / len(data)

import random

STEPS = 10000
best_a, best_b, best_e = None, None, None

# Generate random hypothesis within [0, 1] uniformly.
for i in range(STEPS):
  a = random.uniform(0, 1)
  b = random.uniform(0, 1)
  e_i = calc_e(a, b)

  # Save the best hypothesis with minimum error rate
  if best_e is None or e_i < best_e:
    best_e, best_a, best_b = e_i, a, b

  
print(f"A: {best_a}\nB: {best_b}\nE: {best_e}")