visualization.py

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report

# Load the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train a Random Forest Classifier
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)
# Feature importance
importance = rf_model.feature_importances_
features = iris.feature_names

# Plot the feature importance
plt.figure(figsize=(8, 6))
plt.barh(np.arange(len(features)), importance, align='center')
plt.yticks(np.arange(len(features)), features)
plt.xlabel('Feature Importance')
plt.title('Random Forest Feature Importance')
plt.show()