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plt.legend()
plt.savefig("Linear_Regression.png")
plt.show()
Output:
First 10 actual prices vs predicted prices:
Actual: 28999, Predicted: 33461.01675564506
Actual: 30999, Predicted: 32409.481421638382
Actual: 72999, Predicted: 23646.686971582698
Actual: 27990, Predicted: 35914.599201660654
Actual: 11990, Predicted: 32409.481421638382
Actual: 39990, Predicted: 46429.95254172747
Actual: 56990, Predicted: 39419.71698168293
Actual: 8199, Predicted: 32409.481421638382
Actual: 59999, Predicted: 36790.87864666622
Actual: 10999, Predicted: 32409.481421638382
25. Write Python program for K Means Clustering Algorithm. You may generate synthetic data for 100 loan applicants
with two features: ‘Annual Income’ and ‘Loan Amount’.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Step 1: Create synthetic data
np.random.seed(42)
num_applicants = 100
# Assume we have two features: 'Annual Income' and 'Loan Amount'
annual_income = np.random.normal(loc=50000, scale=15000, size=num_applicants)
loan_amount = np.random.normal(loc=20000, scale=7000, size=num_applicants)
# Combine into a DataFrame
data = pd.DataFrame({
'Annual Income': annual_income,
'Loan Amount': loan_amount })
# Step 2: Standardize the data
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)
Practical Questions 441

