Y más regresiones

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score


metros_cuadrados = [
    40, 42, 45, 48, 50, 52, 55, 58, 60, 62,
    65, 68, 70, 72, 75, 78, 80, 82, 85, 88,
    90, 92, 95, 98, 100, 102, 105, 108, 110, 112,
    115, 118, 120, 122, 125, 128, 130, 132, 135, 138,
    140, 142, 145, 148, 150, 152, 155, 158, 160, 165
]

precio_miles_euros = [
    120, 125, 128, 135, 140, 145, 150, 158, 160, 168,
    175, 180, 185, 190, 198, 205, 210, 215, 223, 230,
    235, 240, 248, 255, 260, 265, 273, 280, 285, 290,
    298, 305, 310, 315, 323, 330, 335, 340, 348, 355,
    360, 365, 373, 380, 385, 390, 398, 405, 410, 425
]

x = np.array(metros_cuadrados).reshape(-1, 1)
y = np.array(precio_miles_euros)

# Datos de entrenamiento y pruebas (split)

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

# Crear modelo y entrenar (fit)

model = LinearRegression()
model.fit(X_train, y_train)

print(f"Pendiente {model.coef_} intercepto {model.intercept_}")

# Predecir datos de test

y_pred = model.predict(X_test)
print(y_test)
print(y_pred.round(0))

# Métricas de errores

print(mean_absolute_error(y_test, y_pred))
print(mean_squared_error(y_test, y_pred))
print(r2_score(y_test, y_pred))