Classify this tweet as either political or non-political.

President Biden
@POTUS
·
4h
Our economy should be one that helps to fill up kitchen cabinets, not the pockets of the ultra-wealthy.
The tweet is political, reflecting the president's views on economic policy. keywords: President Biden, @POTUS, One that helps, pockets of ultra-wealthy, economic policy.??
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The text is classified as a review. It evaluates the quality and  experience of a product, providing specific details about its features, performance, and overall satisfaction. Keywords: 'This product', 'quality of materials', 'easy to use', 'good value', 'recommend to others'.?
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 import pandas as pd
 import numpy as np
 from sklearn.model_selection import train_test_split
 import matplotlib.pyplot as plt
 import seaborn as sns
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import classification_report
 from sklearn.model_selection import cross_val_score
 from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score

def build_model(X_train, y_train):
    model = DecisionTreeClassifier()
    model.fit(X_train, y_train)
    return model

def evaluate_model(model, X_test, y_test):
    predictions = model.predict(X_test)
    accuracy = precision_score(y_test, predictions)
    recall = recall_score(y_test, predictions)
    f1 = f1_score(y_test, predictions)
    return accuracy, recall, f1

data = pd.read_csv('customer_data.csv')
X_train, X_test, y_train, y_test = train_test_split(data, data['Customer Type'], test_size=0.2)
data['Customer Type'].unique()
data['Customer Type'] = data['Customer Type'].map(LabelEncoder().fit_transform)
dt_model = build_model(X_train, y_train)
accuracy, recall, f1 = evaluate_model(dt_model, X_test, y_test)
print(classification_report(y_test, predictions))

# Plot the ROC Curve
y_probs = dt_model.predict_proba(X_test)[:, 1]
fpr, tpr, _ = metrics.roc_curve(y_test, y_probs)
plt.figure(figsize=(10, 8))

plt.plot(fpr, tpr, label='Dummy Classifier')
plt.title('Receiver Operating Characteristic Curve')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend()
plt.show()?
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import scikit-learn

# Load the dataset and split it into training and testing sets
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

iris_dataset = load_iris()
X, y = iris_dataset.data, iris_dataset.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Build a classifier and fit it to the training data
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score

logistic_regressor = LogisticRegression()

cross_val_score(logistic_regressor, X_train, y_train, cv=5, scoring='accuracy')

# Evaluate the trained model on the test data
logistic_regressor.fit(X_train, y_train)
accuracy = logistic_regressor.score(X_test, y_test)
recall = logistic_regressor.score(X_test, y_test)
f1 = logistic_regressor.score(X_test, y_test)

# Print the classification report
print(classification_report(y_test, predictions))

# Plot the ROC Curve
from sklearn.metrics import roc_curve

y_probs = logistic_reg