Feature Engineering

Make better features = Better AI.

What is Feature Engineering?

Creating new features from existing data to improve model performance.

Like giving AI better hints!

Why Important?

Good features > Complex models

Simple model + Great features > Complex model + Bad features

Common Techniques

1. Creating New Features

# From date, create multiple features
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day_of_week'] = df['date'].dt.dayofweek
df['is_weekend'] = df['day_of_week'].isin([5, 6])

2. Combining Features

# House data
df['price_per_sqft'] = df['price'] / df['sqft']
df['total_rooms'] = df['bedrooms'] + df['bathrooms']

3. Binning

# Age groups
df['age_group'] = pd.cut(df['age'], 
                         bins=[0, 18, 35, 50, 100],
                         labels=['child', 'young', 'middle', 'senior'])

4. Encoding Categories

# One-hot encoding
df_encoded = pd.get_dummies(df, columns=['city'])
# Miami → [1, 0, 0]
# Austin → [0, 1, 0]
# Denver → [0, 0, 1]

Real Example - House Prices

Original features: size, bedrooms

New features:

• price_per_sqft = price / size
• has_garage = bedrooms > 2
• age_of_house = current_year - built_year

Feature Scaling

from sklearn.preprocessing import StandardScaler

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

Feature Selection

Remove features that don't help:

from sklearn.feature_selection import SelectKBest

selector = SelectKBest(k=10)
X_selected = selector.fit_transform(X, y)

Remember

• Domain knowledge helps
• Try different combinations
• Remove correlated features
• Scale numeric features