Data Science — Feature Engineering

Encoding categorical variables

import pandas as pd

# One-hot encoding
df = pd.get_dummies(df, columns=['category'])

# Label encoding
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['category_encoded'] = le.fit_transform(df['category'])

Scaling features

from sklearn.preprocessing import StandardScaler, MinMaxScaler

# Standard scaling
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Min-max scaling
scaler = MinMaxScaler()
X_normalized = scaler.fit_transform(X)

Feature creation

# Date features
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day_of_week'] = df['date'].dt.dayofweek

# Interaction features
df['feature_interaction'] = df['feature1'] * df['feature2']

# Polynomial features
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)

Handling missing values

# Fill with mean
df.fillna(df.mean(), inplace=True)

# Fill with median
df.fillna(df.median(), inplace=True)

# Drop rows
df.dropna(inplace=True)

Feature selection

from sklearn.feature_selection import SelectKBest, f_classif

selector = SelectKBest(f_classif, k=5)
X_selected = selector.fit_transform(X, y)

Mini Practice

1. Encode categorical features
2. Scale numerical features
3. Create new features
4. Select important features

Up Next

Continue with Data Visualization - Charts and graphs.