Dimensionality Reduction

In the previous lessons, we explored clustering techniques such as K-Means and Hierarchical Clustering. These methods helped us discover natural groupings inside the data.

While working with real datasets, another important challenge quickly appears. As datasets grow, the number of features can become very large.

In this lesson, we address that challenge using a concept called Dimensionality Reduction.

Why Dimensionality Reduction Is Needed

More features do not always mean better models. In fact, too many features can confuse a machine learning algorithm.

Some features may be redundant. Some may contain noise. Some may provide very little useful information.

Dimensionality reduction helps us simplify the dataset while preserving as much meaningful information as possible.

This leads to faster training, better generalization, and easier visualization.

Understanding the Idea Intuitively

Imagine you are evaluating customers using ten different characteristics. If five of those characteristics tell almost the same story, keeping all ten adds unnecessary complexity.

Dimensionality reduction combines related information and represents it using fewer dimensions.

The goal is not to lose important patterns, but to remove redundancy.

Using Our Dataset

We continue using the same dataset that has guided us through this module.

Dataplexa ML Housing & Customer Dataset

For dimensionality reduction, we focus only on the input features and ignore the loan approval label.

Preparing the Data

Dimensionality reduction techniques rely on numerical relationships. Proper scaling is therefore essential.

import pandas as pd
from sklearn.preprocessing import StandardScaler

df = pd.read_csv("dataplexa_ml_housing_customer_dataset.csv")

X = df.drop("loan_approved", axis=1)

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

What Dimensionality Reduction Achieves

After scaling, the dataset exists in a multi-dimensional space. Each feature adds one dimension.

Dimensionality reduction transforms this space into a smaller number of dimensions that still capture the main structure of the data.

These new dimensions are not original features, but meaningful combinations of them.

Real-World Interpretation

Banks often analyze hundreds of customer attributes. Instead of working with all of them, analysts reduce the data to a smaller set that represents overall risk, stability, and behavior.

This simplifies modeling and decision-making.

Mini Practice

Think about which customer features in our dataset might convey similar information.

For example, income and savings may both indicate financial strength. Dimensionality reduction helps combine such signals.

Exercises

Quick Quiz

In the next lesson, we will study Principal Component Analysis (PCA) and apply dimensionality reduction mathematically.