Linear regression is a fundamental statistical and machine learning technique. It is used to predict a continuous target variable based on one or more predictor variables. This tutorial will guide you through the basics of linear regression, including how to fit a model, interpret the results, and make predictions.

Introduction

Linear regression models the relationship between a dependent variable and one or more independent variables. The simplest form of linear regression is simple linear regression, which involves only one independent variable.

Key Concepts

  • Dependent Variable: The variable you want to predict.
  • Independent Variable(s): The variables used to predict the dependent variable.
  • Coefficient: The slope of the regression line.
  • Intercept: The y-intercept of the regression line.

Steps to Fit a Linear Regression Model

  1. Data Preparation: Collect and prepare your data. Ensure that your data is clean and well-formatted.
  2. Split Data: Split your data into training and testing sets.
  3. Model Fitting: Use a linear regression algorithm to fit a model to your training data.
  4. Model Evaluation: Evaluate the performance of your model using the testing set.
  5. Interpret Results: Interpret the coefficients and intercept of the model.

Example

Suppose you have a dataset with two variables: hours_studied and test_score. You want to predict the test score based on the number of hours studied.

from sklearn.linear_model import LinearRegression

# Create a linear regression model
model = LinearRegression()

# Fit the model to the training data
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

Further Reading

To dive deeper into linear regression, we recommend reading the following tutorials:

Linear Regression Diagram