Machine learning involves building models that can learn patterns from data and make predictions. Here's a simple example of a linear regression model using Scikit-Learn:
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# Split data into features and target
X = data[['feature1', 'feature2']]
y = data['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)
# Create and train a linear regression model
model = LinearRegression()
model.fit(X_train, y_train)
# Make predictions
predictions = model.predict(X_test)
# Calculate mean squared error
mse = mean_squared_error(y_test, predictions)
print(f"Mean Squared Error: {mse}")
