The future of Python Notebooks

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Load the Iris dataset
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target

# Create a DataFrame for better visualization
df = pd.DataFrame(X, columns=iris.feature_names)
df['species'] = pd.Categorical.from_codes(y, iris.target_names)

# Display the first few rows
df.head()

# 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
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Calculate accuracy
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print({"Model accuracy: {accuracy:.2f}"})