Task3

import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import pandas as pd
data_url = "http://lib.stat.cmu.edu/datasets/boston"
raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
target = raw_df.values[1::2, 2]

# Load the Boston housing dataset
 
X = data
y = target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the linear regression model
model = LinearRegression()

# Fit the model on the training data
model.fit(X_train, y_train)

# Predict on the training and testing data
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_test)

# Calculate the scores
train_score = model.score(X_train, y_train)
test_score = model.score(X_test, y_test)

print("Training score:", train_score)
print("Testing score:", test_score)

# Plot residuals
plt.scatter(y_train_pred, y_train_pred - y_train, c='blue', marker='o', label='Training data')
plt.scatter(y_test_pred, y_test_pred - y_test, c='lightgreen', marker='s', label='Testing data')
plt.xlabel('Predicted values')
plt.ylabel('Residuals')
plt.legend(loc='upper left')
plt.hlines(y=0, xmin=0, xmax=50, lw=2, color='red')
plt.title('Residual plot')
plt.show()