Machine Learning in R

Machine Learning is a method where computers learn patterns from data and make decisions or predictions without being explicitly programmed for every rule.

Instead of writing instructions step by step, we provide data and let the algorithm learn from it.

What Is Machine Learning?

Machine Learning allows systems to improve automatically through experience.

It is widely used in prediction, classification, recommendation systems, and intelligent automation.

Types of Machine Learning

Machine learning is generally divided into three main categories.

• Supervised Learning – Uses labeled data
• Unsupervised Learning – Finds patterns in unlabeled data
• Reinforcement Learning – Learns through rewards and penalties

Supervised Learning

In supervised learning, the model is trained using input data along with correct output labels.

The model learns the relationship between inputs and outputs to make future predictions.

Examples: predicting prices, classifying emails, exam result prediction.

Unsupervised Learning

Unsupervised learning works with data that has no labels.

The goal is to discover hidden patterns or groupings within the data.

Examples: clustering customers, grouping similar records.

Common Machine Learning Tasks

• Classification
• Regression
• Clustering
• Prediction
• Pattern Detection

Preparing Data for Machine Learning

Before building a model, data must be cleaned and prepared.

This includes handling missing values, selecting features, and converting data into the correct format.

data <- na.omit(data)
summary(data)

Simple Example: Linear Regression

Linear regression is one of the simplest machine learning algorithms.

It is used to predict a numeric value based on one or more inputs.

model <- lm(y ~ x, data = dataset)
summary(model)

Making Predictions

Once the model is trained, it can be used to predict new values.

Predictions are based on patterns learned during training.

predict(model, newdata = dataset)

Evaluating Model Performance

Evaluating a model helps us understand how accurate it is.

Common evaluation methods include error calculation and comparison with actual values.

Why R Is Good for Machine Learning

• Strong statistical foundation
• Powerful data handling
• Rich ecosystem of packages
• Excellent visualization support

📝 Practice Exercises

Exercise 1

Explain what machine learning means in your own words.

Exercise 2

List the types of machine learning.

Exercise 3

Create a simple linear regression model.

Exercise 4

Make predictions using a trained model.

✅ Practice Answers

Answer 1

Machine learning allows computers to learn patterns from data and improve performance without explicit programming.

Answer 2

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Answer 3

model <- lm(output ~ input, data = dataset)

Answer 4

predict(model, newdata = dataset)

What’s Next?

In the next lesson, you will learn about Classification in R, where models assign data into categories.