Feature Engineering in Python

1. Introduction

Feature engineering is a crucial step in the data preprocessing phase of machine learning. It involves creating new input variables (features) from the raw data to improve model performance.

2. What is Feature Engineering?

Feature engineering is the process of using domain knowledge to extract features from raw data. It can significantly influence the performance of machine learning algorithms.

3. Types of Features

• Numerical Features
• Categorical Features
• Temporal Features
• Text Features
• Image Features

4. Feature Engineering Techniques

Here are some common techniques used in feature engineering:

1. Normalization: Scaling numerical features to a standard range.
2. Encoding: Converting categorical variables into numerical form.
3. Feature Creation: Deriving new features from existing ones.
4. Handling Missing Values: Imputing or removing missing data.

Example: Encoding Categorical Features with One-Hot Encoding

import pandas as pd

# Sample data
data = {'Color': ['Red', 'Blue', 'Green', 'Blue'],
        'Value': [10, 20, 15, 25]}
df = pd.DataFrame(data)

# One-Hot Encoding
df_encoded = pd.get_dummies(df, columns=['Color'])
print(df_encoded)

5. Best Practices

• Understand your data and its domain.
• Always visualize your features.
• Test multiple feature engineering techniques to find the best one.
• Keep your feature set as small as possible to avoid overfitting.

6. FAQ