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name: Feature Engineering description: Create and transform features using encoding, scaling, polynomial features, and domain-specific transformations for improved model performance and interpretability

Feature Engineering

Overview

Feature engineering creates and transforms features to improve model performance, interpretability, and generalization through domain knowledge and mathematical transformations.

When to Use

• When you need to improve model performance beyond using raw features
• When dealing with categorical variables that need encoding for ML algorithms
• When features have different scales and require normalization
• When creating domain-specific features based on business knowledge
• When handling skewed distributions or non-linear relationships
• When preparing data for different types of ML algorithms with specific requirements

Engineering Techniques

• Encoding: Converting categorical to numerical
• Scaling: Normalizing feature ranges
• Polynomial Features: Higher-order terms
• Interactions: Combining features
• Domain-specific: Business-relevant transformations
• Temporal: Time-based features

Key Principles

• Create features based on domain knowledge
• Remove redundant features
• Scale features appropriately
• Handle categorical variables
• Create meaningful interactions

Implementation with Python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import (
    StandardScaler, MinMaxScaler, RobustScaler, PolynomialFeatures,
    OneHotEncoder, OrdinalEncoder, LabelEncoder
)
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
import seaborn as sns

# Create sample dataset
np.random.seed(42)
df = pd.DataFrame({
    'age': np.random.uniform(18, 80, 1000),
    'income': np.random.uniform(20000, 150000, 1000),
    'experience_years': np.random.uniform(0, 50, 1000),
    'category': np.random.choice(['A', 'B', 'C'], 1000),
    'city': np.random.choice(['NYC', 'LA', 'Chicago'], 1000),
    'purchased': np.random.choice([0, 1], 1000),
})

print("Original Data:")
print(df.head())
print(df.info())

# 1. Categorical Encoding
# One-Hot Encoding
print("\n1. One-Hot Encoding:")
df_ohe = pd.get_dummies(df, columns=['category', 'city'], drop_first=True)
print(df_ohe.head())

# Ordinal Encoding
print("\n2. Ordinal Encoding:")
ordinal_encoder = OrdinalEncoder()
df['category_ordinal'] = ordinal_encoder.fit_transform(df[['category']])
print(df[['category', 'category_ordinal']].head())

# Label Encoding
print("\n3. Label Encoding:")
le = LabelEncoder()
df['city_encoded'] = le.fit_transform(df['city'])
print(df[['city', 'city_encoded']].head())

# 2. Feature Scaling
print("\n4. Feature Scaling:")
X = df[['age', 'income', 'experience_years']].copy()

# StandardScaler (mean=0, std=1)
scaler = StandardScaler()
X_standard = scaler.fit_transform(X)

# MinMaxScaler [0, 1]
minmax_scaler = MinMaxScaler()
X_minmax = minmax_scaler.fit_transform(X)

# RobustScaler (resistant to outliers)
robust_scaler = RobustScaler()
X_robust = robust_scaler.fit_transform(X)

# Visualization
fig, axes = plt.subplots(2, 2, figsize=(12, 8))

axes[0, 0].hist(X['age'], bins=30, edgecolor='black')
axes[0, 0].set_title('Original Age')

axes[0, 1].hist(X_standard[:, 0], bins=30, edgecolor='black')
axes[0, 1].set_title('StandardScaler Age')

axes[1, 0].hist(X_minmax[:, 0], bins=30, edgecolor='black')
axes[1, 0].set_title('MinMaxScaler Age')

axes[1, 1].hist(X_robust[:, 0], bins=30, edgecolor='black')
axes[1, 1].set_title('RobustScaler Age')

plt.tight_layout()
plt.show()

# 3. Polynomial Features
print("\n5. Polynomial Features:")
X_simple = df[['age']].copy()
poly = PolynomialFeatures(degree=2, include_bias=False)
X_poly = poly.fit_transform(X_simple)
X_poly_df = pd.DataFrame(X_poly, columns=['age', 'age^2'])
print(X_poly_df.head())

# Visualization
plt.figure(figsize=(12, 5))
plt.scatter(df['age'], df['income'], alpha=0.5)
plt.xlabel('Age')
plt.ylabel('Income')
plt.title('Age vs Income')
plt.grid(True, alpha=0.3)
plt.show()

# 4. Feature Interactions
print("\n6. Feature Interactions:")
df['age_income_interaction'] = df['age'] * df['income'] / 10000
df['age_experience_ratio'] = df['age'] / (df['experience_years'] + 1)
print(df[['age', 'income', 'age_income_interaction', 'age_experience_ratio']].head())

# 5. Domain-specific Transformations
print("\n7. Domain-specific Features:")
df['age_group'] = pd.cut(df['age'], bins=[0, 30, 45, 60, 100],
                          labels=['Young', 'Middle', 'Senior', 'Retired'])
df['income_level'] = pd.qcut(df['income'], q=3, labels=['Low', 'Medium', 'High'])
df['log_income'] = np.log1p(df['income'])
df['sqrt_experience'] = np.sqrt(df['experience_years'])

print(df[['age', 'age_group', 'income', 'income_level', 'log_income']].head())

# 6. Temporal Features (if date data available)
print("\n8. Temporal Features:")
dates = pd.date_range('2023-01-01', periods=len(df))
df['date'] = dates
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day_of_week'] = df['date'].dt.dayofweek
df['quarter'] = df['date'].dt.quarter
df['is_weekend'] = df['date'].dt.dayofweek >= 5

print(df[['date', 'year', 'month', 'day_of_week', 'is_weekend']].head())

# 7. Feature Standardization Pipeline
print("\n9. Feature Engineering Pipeline:")

# Separate numerical and categorical features
numerical_features = ['age', 'income', 'experience_years']
categorical_features = ['category', 'city']

# Create preprocessing pipeline
preprocessor = ColumnTransformer(
    transformers=[
        ('num', StandardScaler(), numerical_features),
        ('cat', OneHotEncoder(drop='first'), categorical_features),
    ]
)

X_processed = preprocessor.fit_transform(df[numerical_features + categorical_features])
print(f"Processed shape: {X_processed.shape}")

# 8. Feature Statistics
print("\n10. Feature Statistics:")
X_for_stats = df[numerical_features].copy()
X_for_stats['category_A'] = (df['category'] == 'A').astype(int)
X_for_stats['city_NYC'] = (df['city'] == 'NYC').astype(int)

feature_stats = pd.DataFrame({
    'Feature': X_for_stats.columns,
    'Mean': X_for_stats.mean(),
    'Std': X_for_stats.std(),
    'Min': X_for_stats.min(),
    'Max': X_for_stats.max(),
    'Skewness': X_for_stats.skew(),
    'Kurtosis': X_for_stats.kurtosis(),
})

print(feature_stats)

# 9. Feature Correlations
fig, axes = plt.subplots(1, 2, figsize=(14, 5))

X_numeric = df[numerical_features].copy()
X_numeric['purchased'] = df['purchased']
corr_matrix = X_numeric.corr()

sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', center=0, ax=axes[0])
axes[0].set_title('Feature Correlation Matrix')

# Distribution of engineered features
axes[1].hist(df['age_income_interaction'], bins=30, edgecolor='black', alpha=0.7)
axes[1].set_title('Age-Income Interaction Distribution')
axes[1].set_xlabel('Value')
axes[1].set_ylabel('Frequency')

plt.tight_layout()
plt.show()

# 10. Feature Binning / Discretization
print("\n11. Feature Binning:")
df['age_bin_equal'] = pd.cut(df['age'], bins=5)
df['age_bin_quantile'] = pd.qcut(df['age'], q=5)
df['income_bins'] = pd.cut(df['income'], bins=[0, 50000, 100000, 150000])

print("Equal Width Binning:")
print(df['age_bin_equal'].value_counts().sort_index())

print("\nEqual Frequency Binning:")
print(df['age_bin_quantile'].value_counts().sort_index())

# 11. Missing Value Creation and Handling
print("\n12. Missing Value Imputation:")
df_with_missing = df.copy()
missing_indices = np.random.choice(len(df), 50, replace=False)
df_with_missing.loc[missing_indices, 'age'] = np.nan

# Mean imputation
age_mean = df_with_missing['age'].mean()
df_with_missing['age_imputed_mean'] = df_with_missing['age'].fillna(age_mean)

# Median imputation
age_median = df_with_missing['age'].median()
df_with_missing['age_imputed_median'] = df_with_missing['age'].fillna(age_median)

# Forward fill
df_with_missing['age_imputed_ffill'] = df_with_missing['age'].fillna(method='ffill')

print(df_with_missing[['age', 'age_imputed_mean', 'age_imputed_median']].head(10))

print("\nFeature Engineering Complete!")
print(f"Original features: {len(df.columns) - 5}")
print(f"Final features available: {len(df.columns)}")

Best Practices

• Understand your domain before engineering features
• Create features that are interpretable
• Avoid data leakage (using future information)
• Test feature importance after engineering
• Document all transformations
• Use appropriate scaling for different algorithms

Common Transformations

• Log Transform: For skewed distributions
• Polynomial Features: For non-linear relationships
• Interaction Terms: For combined effects
• Binning: For categorical approximation
• Normalization: For comparison across scales

Deliverables

• Engineered feature dataset
• Feature transformation documentation
• Correlation analysis of new features
• Distribution comparisons (before/after)
• Feature importance rankings
• Preprocessing pipeline code
• Data dictionary with feature descriptions