Day-11 – Feature Engineering + Data Cleaning

Welcome to Day-11 – Feature Engineering + Data Cleaning (REAL INDUSTRY WORK)

Today is SUPER important.

Most beginners think:

“AI = model training”

But real-world reality:

70% Data Cleaning
20% Feature Engineering
10% Model Training

Today you learn what real AI engineers do daily.

🎯 Goal of Day-11

You will:

✅ Clean messy data
✅ Handle missing values
✅ Create better features
✅ Improve ML performance

🧠 What is Feature Engineering?

Simple meaning:

Creating better input data for ML models

Example:

Instead of:

Date = 2026-05-12

Create:

Year = 2026
Month = 5
Day = 12
Weekend = Yes/No

👉 Better information for model.

🧠 Why Data Cleaning Matters

Real datasets contain:

❌ Missing values
❌ Duplicate rows
❌ Wrong data types
❌ Outliers
❌ Extra spaces

AI engineers clean all this first.

🚀 Part 1 – Create Messy Dataset

import pandas as pd
import numpy as np

data = {
"Name": ["A", "B", "C", "D", "D"],
"Age": [20, 21, np.nan, 23, 23],
"Marks": [70, 85, 90, np.nan, np.nan]
}

df = pd.DataFrame(data)

print(df)

🚀 Part 2 – Check Missing Values

print(df.isnull())

Count missing values:

print(df.isnull().sum())

🚀 Part 3 – Handle Missing Values

✅ Option 1 – Fill with Mean

df["Age"] = df["Age"].fillna(df["Age"].mean())

df["Marks"] = df["Marks"].fillna(df["Marks"].mean())

✅ Option 2 – Remove Missing Rows

df = df.dropna()

⚠ Use carefully in real projects.

🚀 Part 4 – Remove Duplicates

df = df.drop_duplicates()

🚀 Part 5 – Feature Engineering

Create new column:

df["Pass"] = df["Marks"] > 75

print(df)

🧠 What Happened?

You converted raw marks into:

True / False

This is feature engineering.

🚀 Part 6 – Convert Boolean to Numeric

df["Pass"] = df["Pass"].astype(int)

print(df)

Output:

1 = Pass
0 = Fail

🚀 Part 7 – Create Category Feature

df["Performance"] = np.where(
df["Marks"] > 80,
"Good",
"Average"
)

print(df)

🧠 Real AI Examples

Feature engineering examples:

Raw Data                  Engineered Feature

DOB                          Age

Timestamp                Hour / Weekend

Salary                       Salary Range

Text                          Word Count

⚠ Important Interview Question

Q:

Why is feature engineering important?

Answer:

Better features improve model performance and help models learn meaningful patterns.

🧠 Real AI Insight

Sometimes:

• Better feature engineering
  beats
• More advanced models

🎯 End of Day-11 Goals

You now:

✅ Clean data
✅ Handle missing values
✅ Engineer features
✅ Prepare real-world datasets

Github Link : https://github.com/dotnetfullstackdeveloper/ai-engineer-journey/blob/main/Week-02-Machine-Learning/Day-11%3A%20Feature%20Engineering%20%2B%20Data%20Cleaning