Aprendizagem de Maquinas para qualquer sistema

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Sep 22nd, 2024

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Python 1.13 KB | Cybersecurity | 0 0

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix
data = load_iris()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = pd.Series(data.target)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred, average='weighted')
conf_matrix = confusion_matrix(y_test, y_pred)
print(f"Acurácia: {accuracy:.2f}")
print(f"F1 Score: {f1:.2f}")
print("Matriz de Confusão:\n", conf_matrix)
plt.figure(figsize=(8, 6))
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',
xticklabels=data.target_names,
yticklabels=data.target_names)
plt.ylabel('Real')
plt.xlabel('Previsto')
plt.title('Matriz de Confusão')
plt.show()

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