🔵 Data Science · Lesson 45

Feature Engineering

What is Feature Engineering?

Feature Engineering means feature engineering creates useful input columns from raw data to improve model performance.

In real programs, this topic helps in creating better input columns. Learn the idea first, then type the program yourself and compare the output.

💡 At a Glance

Point	Details
Course Area	Data Science Tools and concepts used to analyse, clean and present data.
Main Use	creating better input columns
Example File	`feature-engineering.py`
Practice Focus	Run, change values, and explain the output line by line.

Why should you learn this?

It is useful for creating better input columns.
It connects with encoding categories.
It improves your ability to read, write and debug Python programs.

Important Terms

These terms are used directly in this lesson. Understand them before memorising the code.

Term	Meaning
feature	Input column used for analysis or model training.
encoding	Converting categorical text values into numeric form.
scaling	Putting numeric values into a comparable range.
new column	new column is an important term in this topic.
model input	model input is an important term in this topic.

Syntax / Basic Pattern

The simple pattern is: prepare data, apply the concept, then show the result.

Basic Pattern

import pandas as pd
df = pd.DataFrame({"StudyHours": [2, 4, 6], "Attendance": [70, 85, 95]})
df["Study_Attendance_Score"] = df["StudyHours"] * df["Attendance"]
print(df)

Complete Example Program

Python – feature-engineering.py

import pandas as pd

df = pd.DataFrame({"StudyHours": [2, 4, 6], "Attendance": [70, 85, 95]})

df["Study_Attendance_Score"] = df["StudyHours"] * df["Attendance"]
print(df)

Expected Output

StudyHours Attendance Study_Attendance_Score 0 2 70 140 1 4 85 340 2 6 95 570

Program Explanation

import pandas as pd imports ready-made features from a module/library.
df = pd.DataFrame({"StudyHours": [2, 4, 6], "Attendance": [70, 85, 95]}) stores a value in df.
df["Study_Attendance_Score"] = df["StudyHours"] * df["Attendance"] stores a value in df["Study_Attendance_Score"].
print(df) displays information or calculated result on the screen.

Where will you use it?

Creating better input columns.
Encoding categories.
Improving model performance.

Common Mistakes

Analysing data before checking missing values, duplicates and data types.
Changing original data without keeping a clean copy.
Creating charts without title, labels or explanation.

Practice Tasks

Type the program in feature-engineering.py and run it.
Change input values or sample data and observe the new output.
Create one example related to creating better input columns.
Write 5 lines explaining the logic in your own words.

Summary

Feature Engineering is not a theory-only topic. You should be able to explain the meaning, write the example, run it successfully, and use it in a small practical program.