import matplotlib.pyplot as plt

import pandas as pd

# Data for Logistic Regression

data_logistic_regression = {

"Step": range(1, 11),

"Accuracy": [63.21, 36.78, 95.89, 95.89, 63.21, 63.21, 95.89, 96.12, 96.69, 96.24]

}

# Data for Support Vector Machine (SVM)

data_svm = {

"Step": range(1, 11),

"Accuracy": [63.21, 63.21, 63.21, 63.21, 96.81, 96.69, 96.58, 96.58, 96.58, 96.58]

}

# Data for Random Forest

data_random_forest = {

"Step": range(1, 15),

"Accuracy": [63.43, 63.55, 63.21, 64.35, 65.71, 75.62, 81.89, 82.68, 94.53, 94.64, 94.64, 94.64, 94.87, 94.53]

}

# Convert to DataFrames

df_logistic_regression = pd.DataFrame(data_logistic_regression)

df_svm = pd.DataFrame(data_svm)

df_random_forest = pd.DataFrame(data_random_forest)

# Plot Accuracy Comparison for Logistic Regression, SVM, and Random Forest

plt.figure(figsize=(12, 6))

# Logistic Regression

plt.plot(df_logistic_regression["Step"], df_logistic_regression["Accuracy"], marker='o', linestyle='-', linewidth=2, color='purple', label="Logistic Regression Accuracy")

# SVM

plt.plot(df_svm["Step"], df_svm["Accuracy"], marker='s', linestyle='--', linewidth=2, color='brown', label="SVM Accuracy")

# Random Forest

plt.plot(df_random_forest["Step"], df_random_forest["Accuracy"], marker='d', linestyle=':', linewidth=2, color='darkgreen', label="Random Forest Accuracy")

plt.xlabel("Step")

plt.ylabel("Accuracy (%)")

plt.title("Accuracy Comparison: Logistic Regression vs. SVM vs. Random Forest")

plt.legend()

plt.grid(True)

plt.show()