5. ML

 import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score, classification_report

data = pd.read_csv("car_evaluation.csv", header=None)

columns = ["buying","maint","doors","persons","lug_boot","safety","class"]

data.columns = columns

data_encoded = pd.get_dummies(data, columns=columns[:-1], drop_first=True)

X=data_encoded.drop('class' , axis = 1)

y=data_encoded['class']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

rf_classifier = RandomForestClassifier(random_state=42)

rf_classifier.fit(X_train, y_train)

y_pred = rf_classifier.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)

print("Accuracy:" , accuracy)

class_report = classification_report(y_test, y_pred, target_names=data['class'].unique())

print("Classification Report:\n", class_report)