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Example 1: Machine Learning – Regression with Regularization
Assignment Task:
“Implement Ridge and Lasso regression from scratch in Python. Compare the models based on RMSE using 10-fold cross-validation. Use the Boston Housing dataset for demonstration.”
Solution Outline:
Our expert handled this assignment by first normalizing the dataset and then implementing Ridge and Lasso regression without relying on external libraries like scikit-learn for model creation. Here's a glimpse into the core solution for Ridge regression:
import numpy as np
from sklearn.datasets import load_boston
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
# Load dataset
data = load_boston()
X = data.data
y = data.target
X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)
# Ridge Regression
def ridge_regression(X, y, alpha):
I = np.identity(X.shape[1])
return np.linalg.inv(X.T @ X + alpha * I) @ X.T @ y
# Cross-validation
kf = KFold(n_splits=10)
rmse_list = []
alpha = 1.0
for train_idx, test_idx in kf.split(X):
X_train, X_test = X[train_idx], X[test_idx]
y_train, y_test = y[train_idx], y[test_idx]
w = ridge_regression(X_train, y_train, alpha)
y_pred = X_test @ w
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
rmse_list.append(rmse)
average_rmse = np.mean(rmse_list)
print("Average RMSE (Ridge):", average_rmse)
The expert provided a similar solution for Lasso using coordinate descent and wrapped both solutions into a concise report comparing performance metrics, as requested. This kind of help with programming assignment ensures students not only get correct answers but also learn best practices.
Example 2: Advanced Java – Multithreading Simulation
Assignment Task:
“Design a multithreaded Java application simulating a ticket booking system where multiple agents attempt to book tickets concurrently. Implement proper synchronization to prevent race conditions.”
Solution Summary:
Our expert developed a simulation using the synchronized
keyword and Thread
class to manage concurrency. Here’s a simplified snippet:
class TicketBooking {
private int ticketsAvailable = 5;
public synchronized void bookTicket(String agentName, int requestedTickets) {
if (requestedTickets <= ticketsAvailable) {
System.out.println(agentName + " booked " + requestedTickets + " ticket(s).");
ticketsAvailable -= requestedTickets;
} else {
System.out.println(agentName + " - Not enough tickets available.");
}
}
}
class Agent extends Thread {
private TicketBooking bookingSystem;
private int tickets;
private String agentName;
public Agent(TicketBooking system, int tickets, String name) {
this.bookingSystem = system;
this.tickets = tickets;
this.agentName = name;
}
public void run() {
bookingSystem.bookTicket(agentName, tickets);
}
}
public class BookingSimulation {
public static void main(String[] args) {
TicketBooking booking = new TicketBooking();
Agent a1 = new Agent(booking, 2, "Agent 1");
Agent a2 = new Agent(booking, 3, "Agent 2");
Agent a3 = new Agent(booking, 2, "Agent 3");
a1.start();
a2.start();
a3.start();
}
}
The expert also included explanations of thread safety, deadlocks, and JVM concurrency tools, presenting it as a fully annotated report.
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