Linear Regression Tutorial

Linear regression is a fundamental concept in machine learning, used for predicting a continuous outcome based on one or more input variables. This tutorial will guide you through the basics of linear regression, including its assumptions, types, and implementation.

Key Concepts

Linear Relationship: The relationship between the input variables and the output variable is linear.
Predictive Model: Linear regression creates a model that can predict the output variable based on the input variables.
Cost Function: The cost function measures the difference between the predicted values and the actual values.

Types of Linear Regression

Simple Linear Regression: One input variable and one output variable.
Multiple Linear Regression: Multiple input variables and one output variable.

Implementation

Here's a simple example of implementing linear regression using Python:

# Importing necessary libraries
import numpy as np
from sklearn.linear_model import LinearRegression

# Creating a dataset
X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
y = np.array([2, 4, 5, 4, 5])

# Creating a linear regression model
model = LinearRegression()

# Training the model
model.fit(X, y)

# Making predictions
predictions = model.predict(X)

# Printing the predictions
print(predictions)

For more information on linear regression, you can visit our Machine Learning Basics tutorial.