visual example of linear regression

lr_plot_ex.py

from sklearn import linear_model
import numpy as np
import matplotlib.pyplot as plt

# generating fake linear data. 
# Where x = square meter, y = house price

cond = False

while not cond:
    x = np.linspace(90, 1200, 100)
    delta = np.random.uniform(0.77, 1.7, x.size)
    gamma = np.random.normal(0, 3, x.size)
    y = delta*500*x + gamma*4500
    x = x.astype("float32")
    x = x.reshape((x.shape[0],1))
    y = y.astype("float32")
    y = y.reshape((y.shape[0],1))
    data = np.concatenate([x,y],axis=1)
    cond = min(y)>0

#fitting the model and saving the weights

lr = linear_model.LinearRegression()
lr.fit(x,y)

if lr.coef_.shape == (2,1):
    w, b = lr.coef_[0][0], lr.coef_[0][1]
else:
    w, b = lr.coef_[0][0], 0

# ploting the prediction and saving the image

fig, ax = plt.subplots(1, 1, figsize=(8, 8))
line1, = ax.plot(x[0:70], y[0:70], 'bo', label='Real data')
line2, = ax.plot(x, x * w + b, 'r', label='Predicted data')
plt.legend(handles=[line1, line2], loc=2)
ax.set_title('House prices prediction',
             fontsize=20,
             fontweight='bold')

ax.set_xlabel("m\u00b2", fontsize=20)
ax.set_ylabel('$', fontsize=20)
plt.savefig("house_prices.png")