Salary_dataset

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

//Generate some dummy data for salary dataset
np.random.seed(0)
years_experience = np.random.randint(1, 20, 100).reshape(-1, 1)
salary = 50000 + 2000 * years_experience + np.random.normal(0, 10000, (100, 1))

//Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(years_experience, salary, test_size=0.2, random_state=42)

//Create and train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

//Print the training and testing sets
print("Training set:")
print("X_train:", X_train[:5])
print("y_train:", y_train[:5])

print("\nTesting set:")
print("X_test:", X_test[:5])
print("y_test:", y_test[:5])

//Make predictions
predictions = model.predict(X_test)

//Evaluate the model (for simplicity, let's just print the first few predictions)
print("\nPredictions:")
print(predictions[:5])