Feature Engineering Techniques

Here are some of the most widely used Feature Engineering techniques, along with simple Python examples for each.

Encoding Categorical Variables

import pandas as pd

df = pd.DataFrame({"City": ["Noida", "Delhi", "Noida"]})

# One-hot encoding
df_encoded = pd.get_dummies(df, columns=["City"])

Scaling Numeric Features

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
df["ScaledIncome"] = scaler.fit_transform(df[["Income"]])

Binning Continuous Values

df["AgeGroup"] = pd.cut(df["Age"], bins=[0, 18, 35, 60, 100],
                        labels=["Teen", "Young Adult", "Adult", "Senior"])

Extracting Date Parts

df["OrderDate"] = pd.to_datetime(df["OrderDate"])
df["Month"] = df["OrderDate"].dt.month
df["Weekday"] = df["OrderDate"].dt.day_name()

Creating Ratio and Interaction Features

df["ExpensePerPerson"] = df["TotalExpense"] / df["FamilySize"]
df["PriceTimesQuantity"] = df["Price"] * df["Quantity"]

Coming Up Next

Next, you'll shift focus to the basics of Probability - a foundation that underpins much of statistics and machine learning.