Feature Engineering in R Programming

Feature Engineering in R means creating new features or modifying existing ones to make models work better. It includes cleaning, transforming, scaling, encoding and selecting features for machine learning.

• Helps models understand data better
• Removes noise and unwanted patterns
• Converts raw data into useful inputs
• Works with both numeric and categorical features

In R, this is done using packages like dplyr, tidyr, caret and data.table.

Sample Dataset

Output:

This dataset has:

• Numeric features: age, income
• Categorical features: gender, city

We will use this small data to explain each concept.

1. Handling Missing Values

The dataset contains a missing value in income.

Example (add NA for explanation):

Output:

Explanation:

• mean(..., na.rm = TRUE) calculates mean without NA.
• Replaces missing entry with the average income.

2. Encoding Categorical Variables

Label Encoding (for binary categories: gender)

Output:

Explanation:

• Male = 1
• Female = 0

One-Hot Encoding (for multi-class: city)

Output:

Explanation:

City A, B and C become separate columns:

• cityA
• cityB
• cityC

Each gets 0/1 depending on membership.

3. Feature Scaling

Scaling helps numeric values stay on similar ranges.

Using standard scaling (mean = 0, sd = 1)

Output:

Explanation:

• Makes numeric features easier for algorithms like KNN, SVM, etc.

4. Binning (Feature Transformation)

Create age groups:

Output:

Explanation:

• Converts continuous age into categories
• Helps models see pattern in ranges

5. Feature Construction

Create a new feature: income per year of age

Output:

6. Removing Skewness

Apply log transformation to reduce skew in income:

Output:

Explanation:

• Helps stabilize values
• Makes distribution smoother

7. Final Cleaned Feature-Enhanced Dataset

After all steps, the dataset now looks like this:

• original variables (age, income, gender, city)
• encoded variables (gender_num, cityA, cityB, cityC)
• scaled variables (age_scaled, income_scaled)
• transformed variables (income_log, age_group)
• constructed feature (income_per_age)

This feature rich dataset is now ready for modeling.