import numpy as np
from sklearn.linear_model import SGDRegressor
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt

# Original data
X = np.array([
    [1100],
    [1400],
    [1425],
    [1550],
    [1600],
    [1700],
    [1700],
    [1875],
    [2350],
    [2450]
])
y = np.array([
    199000,
    245000,
    319000,
    240000,
    312000,
    279000,
    310000,
    308000,
    405000,
    324000
])

# Scale input and output
x_scaler = StandardScaler()
y_scaler = StandardScaler()

X_scaled = x_scaler.fit_transform(X)
y_scaled = y_scaler.fit_transform(y.reshape(-1, 1)).ravel()

# Use SGDRegressor
model = SGDRegressor(max_iter=150, learning_rate='constant', eta0=0.01)
model.fit(X_scaled, y_scaled)

# Predict and convert back to original scale
X_test = np.linspace(min(X_scaled), max(X_scaled), 100).reshape(-1, 1)
y_pred_scaled = model.predict(X_test)
y_pred = y_scaler.inverse_transform(y_pred_scaled.reshape(-1, 1))

# Plot
plt.plot(X, y, 'k^', label='Original Data')
X_orig = x_scaler.inverse_transform(X_test)
plt.plot(X_orig, y_pred, 'r-', label='SGDRegressor Line')
plt.xlabel("Square Footage")
plt.ylabel("Price")
plt.title("SGD Regressor (scikit-learn)")
plt.legend()
plt.show()