this code does what it does, extend it by adding the following functionality, preserve the style of comments seen in the code:
Summarize the results: § Analyze and interpret the results: Which campaign was more effective? Was the difference statistically significant? § Provide recommendations for choosing a marketing campaign.
Key libraries: Pandas, SciPy (for statistical tests), Matplotlib, Seaborn.

code:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats

# Load the data with semicolon delimiter
control_group = pd.read_csv('control_group.csv', delimiter=';')
test_group = pd.read_csv('test_group.csv', delimiter=';')

# Initial data inspection
print("Control Group Data Overview:")
print(control_group.head())
print(control_group.info())
print("\nTest Group Data Overview:")
print(test_group.head())
print(test_group.info())

# Data Preprocessing - Handle missing values and ensure date format
control_group = control_group.dropna()  # Drop rows with any missing values
test_group = test_group.dropna()  # Drop rows with any missing values

# Convert 'Date' column to datetime format
control_group['Date'] = pd.to_datetime(control_group['Date'], errors='coerce', dayfirst=True)
test_group['Date'] = pd.to_datetime(test_group['Date'], errors='coerce', dayfirst=True)

# Drop rows where Date conversion failed
control_group = control_group.dropna(subset=['Date'])
test_group = test_group.dropna(subset=['Date'])

# Verify cleaned data
print("\nCleaned Control Group Data:")
print(control_group.head())
print("\nCleaned Test Group Data:")
print(test_group.head())

# Calculate Conversion Rates
control_group['Conversion Rate'] = control_group['# of Purchase'] / control_group['# of Impressions']
test_group['Conversion Rate'] = test_group['# of Purchase'] / test_group['# of Impressions']

# Display average conversion rates
print("Control Group Conversion Rate:", control_group['Conversion Rate'].mean())
print("Test Group Conversion Rate:", test_group['Conversion Rate'].mean())

# Hypothesis Testing: Two-sample t-test
t_stat, p_value = stats.ttest_ind(control_group['Conversion Rate'], test_group['Conversion Rate'], equal_var=False)
print("\nT-test Results")
print("T-statistic:", t_stat)
print("P-value:", p_value)

# Interpret significance
if p_value < 0.05:
    print("The difference in conversion rates is statistically significant (p < 0.05).")
else:
    print("No statistically significant difference in conversion rates (p ≥ 0.05).")

# Visualization
plt.figure(figsize=(15, 5))

# Bar plot of average conversion rates
plt.subplot(1, 3, 1)
conversion_means = [control_group['Conversion Rate'].mean(), test_group['Conversion Rate'].mean()]
campaigns = ['Control', 'Test']
sns.barplot(x=campaigns, y=conversion_means)
plt.title('Average Conversion Rate')
plt.ylabel('Conversion Rate')

# Boxplot of conversion rates for both groups
plt.subplot(1, 3, 2)
sns.boxplot(data=[control_group['Conversion Rate'], test_group['Conversion Rate']])
plt.xticks([0, 1], ['Control', 'Test'])
plt.title('Conversion Rate Distribution (Boxplot)')

# Histogram of conversion rates
plt.subplot(1, 3, 3)
sns.histplot(control_group['Conversion Rate'], color='blue', label='Control', kde=True)
sns.histplot(test_group['Conversion Rate'], color='orange', label='Test', kde=True)
plt.legend()
plt.title('Conversion Rate Distribution (Histogram)')

plt.tight_layout()
plt.show()