6a) Demonstrate how to build a linear regression model using Go. Building a linear regression model involves fitting a line to a set of data points to predict the relationship between the dependent variable and one or more independent variables. Here's an example Go program that demonstrates how to build a simple linear regression model using the gradient descent algorithm: package main import ( "fmt" "math" ) func main() { // Example training dataset x := []float64{1, 2, 3, 4, 5} y := []float64{2, 4, 6, 8, 10} // Train the linear regression model theta0, theta1 := trainLinearRegression(x, y, 0.01, 1000) // Predict using the trained model predicted := predictLinearRegression(x, theta0, theta1) // Display the trained model and predictions fmt.Printf("Trained Model: y = %.2f + %.2fx\n", theta0, theta1) fmt.Println("Predicted Values:", predicted) } // Function to train a linear regression model using gradient descent func trainLinearRegression(x, y []float64, learningRate float64, numIterations int) (float64, float64) { m := len(x) theta0 := 0.0 theta1 := 0.0 for i := 0; i < numIterations; i++ { gradient0 := 0.0 gradient1 := 0.0 for j := 0; j < m; j++ { predicted := theta0 + theta1*x[j] gradient0 += (predicted - y[j]) gradient1 += (predicted - y[j]) * x[j] } gradient0 /= float64(m) gradient1 /= float64(m) theta0 -= learningRate * gradient0 theta1 -= learningRate * gradient1 } return theta0, theta1 } // Function to predict values using a trained linear regression model func predictLinearRegression(x []float64, theta0, theta1 float64) []float64 { predicted := make([]float64, len(x)) for i := 0; i < len(x); i++ { predicted[i] = theta0 + theta1*x[i] } return predicted } Note: In this program, w