This video is the practical continuation of the Fundamentals of Data Preparation series.
In the previous video, we covered the key concepts behind data preparation—cleaning, transformation, feature engineering, and data splitting.
Now, we implement everything step-by-step in Python using a real dataset.
🔧 In this video, you’ll learn how to:
Load and explore a dataset
Handle missing values properly
Encode categorical features
Scale numerical data
Create new features (feature engineering)
Build a complete preprocessing pipeline
Split data into training and test sets
We’ll use the Titanic dataset as a hands-on example to demonstrate real-world data preparation workflows.
💡 This is essential for anyone working with machine learning, data science, or AI.
📺 If you haven’t watched the first video yet, start here:
👉 https://www.youtube.com/watch?v=7UOJVLDZawY
👍 Like and subscribe for more practical data science tutorials!
Timeline:
0:00 Intro
0:11 Upcoming
0:20 How to Code Data Preparation
0:26 Loading Data
2:30 Select Columns
2:53 Feature Engineering
3:17 Extract Labels
3:48 Data Preprocessing
7:15 Split Data
9:40 Summary
#datapreparation #python #machinelearning #datascience #artificialintelligence
