How can I use scikit-learn for regression analysis?

How can I use scikit-learn for regression analysis?

You can use scikit-learn's LinearRegression class to perform regression analysis. First, you'll need to import the library and load your dataset.

What are the steps to implement a linear regression model?

The steps include: 1) Importing necessary libraries, 2) Loading your data, 3) Splitting the data into training and testing sets, 4) Creating a LinearRegression model, 5) Fitting the model to your training data, and 6) Making predictions.

Can you show me a code example?

Sure! Here's a simple example:

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

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

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

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

# Make predictions
predictions = model.predict(X_test)

What metrics can I use to evaluate the model's performance?

You can use metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared to evaluate your model's performance.