pdfcoffee

Regression
3. Now, we calculate the two regression coefficients using the equations we
defined. You can see the result is very much near the linear relationship we
have simulated:
W = sum(price*(area-np.mean(area))) / sum((area-np.mean(area))**2)
b = np.mean(price) - W*np.mean(area)
print("The regression coefficients are", W,b)
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The regression coefficients are 24.815544052284988
43.4989785533412
4. Let us now try predicting the new prices using the obtained weight and bias
values:
y_pred = W * area + b
5. Next, we plot the predicted prices along with the actual price. You can see
that predicted prices follow a linear relationship with the area:
plt.plot(area, y_pred, color='red',label="Predicted Price")
plt.scatter(data['area'], data['price'], label="Training Data")
plt.xlabel("Area")
plt.ylabel("Price")
plt.legend()
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