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import matplotlib.pyplot as plt

import numpy as np

	
# Line Chart
model_accuracy1 = [0.75, 0.80, 0.85, 0.88, 0.90]
model_accuracy2 = [0.70, 0.75, 0.88, 0.82, 0.85]
epochs = [1, 2, 3, 4, 5]

plt.figure(figsize=(10, 8))

plt.subplot(2, 2, 1)
plt.plot(epochs, model_accuracy1, marker='>', label='Model 1')
plt.plot(epochs, model_accuracy2, marker='+', label='Model 2')
plt.title("Accuracy comparison of two models")
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

# Histogram
data = [1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 8]
plt.subplot(2, 2, 2)
plt.hist(data, bins=8, edgecolor='black')
plt.title("Histogram")
plt.xlabel("Value")
plt.ylabel("Frequency")

# Pie Chart
sizes = [20, 30, 25, 25]
labels = ['A', 'B', 'C', 'D']
Page No.  30
plt.subplot(2, 2, 3)
plt.pie(sizes, labels=labels, autopct='%1.1f%%')
plt.title("Pie Chart")

# Radar Chart
plt.subplot(2, 2, 4, polar=True)

# Number of variables
num_vars = len(epochs)

# Compute angle for each axis
angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()

# Make the plot close to a circle
model_accuracy1 += model_accuracy1[:1]
model_accuracy2 += model_accuracy2[:1]
angles += angles[:1]

plt.plot(angles, model_accuracy1, marker='o', label='Model 1')
plt.plot(angles, model_accuracy2, marker='o', label='Model 2')
plt.title("Radar Chart")
plt.legend()

plt.tight_layout()
plt.show()