Feature Scaling: Techniques and Applications
├── Introduction
│   └── Understanding Feature Scaling
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Generating the Dataset
├── Implementing Feature Scaling
│   ├── Data Preparation
│   ├── Applying Scaling Techniques
│   └── Scaling Data
├── Visualization
│   └── Feature Scaling Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Understanding Feature Scaling

• Feature scaling is a method used to standardize the range of features in a dataset. It's crucial in machine learning models that depend on the magnitude of features.

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.datasets import make_blobs

Generating the Dataset

# Python code to generate a sample dataset
X, _ = make_blobs(n_samples=300, centers=2, random_state=42, cluster_std=5.0)

3. Implementing Feature Scaling

Data Preparation

• Preparing the dataset for scaling involves ensuring data is clean and suitable for scaling methods.

Applying Scaling Techniques

• Standardization and normalization are two primary techniques used in feature scaling.

Scaling Data

# Standardization
scaler = StandardScaler()
X_standardized = scaler.fit_transform(X)

# Normalization
min_max_scaler = MinMaxScaler()
X_normalized = min_max_scaler.fit_transform(X)

4. Visualization

Feature Scaling Visualization