Q.Types of spenders

import matplotlib.pyplot as plt

import seaborn as sns

import pandas as pd

# Calculate the average Annual Income and Spending Score for each Genre

genre_metrics = df.groupby('Genre')[['Annual_Income_(k$)', 'Spending_Score']].mean().reset_index()

# Melt the DataFrame to a long format for plotting

genre_metrics_melted = genre_metrics.melt(id_vars='Genre', var_name='Metric', value_name='Average_Value')

plt.figure(figsize=(10, 6))

sns.barplot(x='Genre', y='Average_Value', hue='Metric', data=genre_metrics_melted, palette='tab10')

plt.title('Average Annual Income and Spending Score by Genre')

plt.xlabel('Genre')

plt.ylabel('Average Value')

plt.legend(title='Metric')

plt.tight_layout()

plt.show()

import matplotlib.pyplot as plt

import seaborn as sns

import pandas as pd

plt.figure(figsize=(8, 6)) # Reduced figure size

sns.scatterplot(x='Annual_Income_(k$)', y='Spending_Score', hue='Age', sizes=(10, 200), data=df, palette='viridis', alpha=0.7) # Adjusted point sizes

plt.title('Spending Score vs. Annual Income, Colored by Age')

plt.xlabel('Annual Income (k$)')

plt.ylabel('Spending Score (1-100)')

plt.legend(title='Age', loc='best') # Simplified legend placement

plt.grid(True, linestyle='--', alpha=0.6)

plt.tight_layout()

plt.show()