b) Demonstrate how to build a multiple linear regression using Go.
Building a multiple linear regression model involves fitting a linear equation to a set of data
points with multiple independent variables to predict the relationship with a dependent variable.
Here’s an example Go program that demonstrates how to build a multiple linear regression
model using the gradient descent algorithm
package main
import
(
"fmt"
)
func main() {
Example training dataset
x1 := []float64{1, 2, 3, 4, 5}
x2 := []float64{0.5, 1, 1.5, 2, 2.5}
y := []float64{3, 5, 7, 9, 11}
// Train the multiple linear regression model
theta0, theta1, theta2 := trainMultipleLinearRegression(x1, x2, y, 0.01, 1000)
// Predict using the trained model
predicted := predictMultipleLinearRegression(x1, x2, theta0, theta1, theta2)
// Display the trained model and predictions
fmt.Printf("Trained Model: y = %.2f + %.2fx1 + %.2fx2\n", theta0, theta1, theta2)
fmt.Println("Predicted Values:", predicted)
}
// Function to train a multiple linear regression model using gradient descent
func trainMultipleLinearRegression(x1, x2, y []float64, learningRate float64,
numIterations int) (float64, float64, float64) {
m := len(x1)
theta0 := 0.0
theta1 := 0.0
theta2 := 0.0
for i := 0; i < numIterations; i++ {
gradient0 := 0.0
gradient1 := 0.0
gradient2 := 0.0
for j := 0; j < m; j++ {
predicted := theta0 + theta1*x1[j] + theta2*x2[j]
gradient0 += (predicted - y[j])
gradient1 += (predicted - y[j]) * x1[j]
gradient2 += (predicted - y[j]) * x2[j]
}
gradient0 /= float64(m)
gradient1 /= float64(m)
gradient2 /= float64(m)
theta0 -= learningRate * gradient0
theta1 -= learningRate * gradient1
theta2 -= learningRate * gradient2
}
return theta0, theta1, theta2
}
Function to predict values using a trained multiple linear regression model
func predictMultipleLinearRegression(x1, x2 []float64, theta0, theta1, theta2 float64)
[]float64 {
predicted := make([]float64, len(x1))
for i := 0; i < len(x1); i++ {
predicted[i] = theta0 + theta1*x1[i] + theta2*x2[i]
}
return predicted
}
Note: In this progra
