Machine Learning with Python: Feature Scaling

Introduction

• Builder ninth video in the series
• Topic: Feature Scaling for your dataset

What is Feature Scaling?

• Converting numerical data in a wide range into the same scale or range
• Example: Columns like mpg, displacement, horsepower, weight, and acceleration with different value ranges
• Goal: Convert numerical data to range between -1 to 1 for uniformity

Why Do We Need Feature Scaling?

Reason 1: Algorithms Using Euclidean Distance

• Euclidean distance formula used by many algorithms
• Issue: Large differences in numerical values cause high uncertainty
• Example: Thousands vs. tens of numerical values creates inaccuracies
• Solution: Feature scaling makes ranges uniform, improving model accuracy

Reason 2: Training Time Efficiency

• Small numerical range (-1 to 1) reduces training time

Methods of Feature Scaling

1. Standardization
2. Normalization

• Both methods provided by Python libraries
• Python package handles calculations automatically

Practical Implementation

• Jupyter notebook used (same as test and train split video)
• Import necessary libraries

Formulas

• Standardization formula provided
• Not focusing on detailed math, as Python handles this

Implementation Steps

1. Create standard scaler class object
2. Apply standard scaler to train data (X_train)
3. Transform train data
4. Apply to test data (X_test)

Results

• Values in each column vary between -1 and 1 for both train and test data

Special Note: Dependent Variable (Y)

• Feature scaling not applied to Y as it contains categorical data
• Y has values 0 (not buy) or 1 (buy)
• Scaling Y could spoil the model

Conclusion

• Final video on data pre-processing
• Next: Supervised and unsupervised machine learning algorithms
• Future videos will focus on machine learning journey

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