Day 25: Feature Engineering Explained (Missing Values & Target Encoding) | Part 1 | 120-Day Series
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120-Day Data Science Series
Welcome to Day 25 (Part 1) of the 120-Day Data Science Series 🚀
In this video, we begin the most important step before Machine Learning — Feature Engineering, using a real-world Heart Disease dataset.
This is not theory-only.
You’ll see practical, industry-style Feature Engineering, exactly how Data Scientists work on real healthcare datasets.
🔥 What You’ll Learn in Day 25 (Part 1)
✅ What is Feature Engineering?
Definition in simple terms
Why Feature Engineering matters more than model choice
Feature Engineering vs Feature Selection
✅ Connecting EDA (Day 24) to Feature Engineering
How EDA insights drive Feature Engineering decisions
Identifying strong vs weak predictors
Why we don’t blindly trust raw features
✅ Handling Missing Values (Real-World Scenario)
Why real datasets always contain missing values
Mean vs Median imputation
Why median is safer in medical data
✅ Date Feature Engineering
Why ML models cannot understand datetime directly
Extracting Year, Month, Day, DayOfWeek
How time-based features help models learn patterns
✅ Target Variable Encoding
Converting categorical target labels into numeric form
Why explicit mapping is safer than automatic encoding
Preparing the target variable for Machine Learning
📘 Dataset Used
Real Kaggle Heart Disease dataset
Modified to simulate real-world data challenges
Same dataset used across EDA → Feature Engineering → ML
▶️ What’s Coming in Part 2 (Day 26)
In Day 26, we will cover:
Categorical encoding (Binary, Ordinal, One-Hot)
Feature scaling (StandardScaler vs MinMaxScaler)
Feature transformation (log, skew handling)
Outlier treatment (medical context)
Feature creation & interaction features
Feature selection basics
ML-ready dataset preparation
🎯 Why This Video Is Important
Most beginners:
❌ Jump straight to Machine Learning
❌ Ignore Feature Engineering
❌ Build weak models
This video teaches you:
✅ How to think like a Data Scientist
✅ How to prepare data before ML
✅ How to avoid beginner mistakes
🔗 Data Set Link : https://www.kaggle.com/datasets/neuro...
🔗 Resources & Links
📘 Notebook / Code Files: https://drive.google.com/drive/folder...
▶️ Watch Previous Days (Full Playlist)
📌 Day 1 – VS Code Installation
📌 Day 2 – Python Basics [ • How to Install VS Code for Python (Step-by... ]
📌 Day 3 – Data Structures + Libraries [ • Day 2: Python Basics for Data Science (Beg... ]
📌 Day 4 – If–Elif–Else Conditions [ • Day 3: Python Data Structures, NumPy & Pan... ]
📌 Day 5 – For Loop [ • Day 4: Python If–Elif–Else Explained for D... ]
📌 Day 6 – For Loop Advance [ • Day 6 : Python For Loop SECRET You Need to... ]
📌 Day 7 – Full While Loop Masterclass [ • Day 7: While Loop Mastery for Data Science... ]
📌 Day 8 – Functions
📌 Day 9 – List Basics
📌 Day 10 – Advanced Lists
📌 Day 11 – Tuples
📌 Day 12 – Dictionaries (Basics)
📌 Day 13 – Advanced Dictionaries
📌 Day 14 – Python Sets
📌 Day 17 – NumPy Basics
📌 Day 18 – Advanced NumPy
🎥 Full Playlist: [ • 120 Day Data Science Series ]
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