Master Python, ML & Data Science

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# Load and prepare data
df = pd.read_csv('data.csv')
X = df[['feature1', 'feature2', 'feature3']]
y = df['target']

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Create and train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

# Evaluate model performance
score = model.score(X_test, y_test)
print(f"Model R² Score: {score:.4f}")