Data Preprocessing Pipeline: Cleaning, Transformation, and Feature Engineering

Welcome to the Gudsky AI & ML Educational Series 🚀

In this session, we explore the end-to-end data preprocessing workflow — the foundation of any successful machine learning project. You’ll learn how to take raw, messy data and transform it into clean, structured, and meaningful input for ML algorithms.

🔍 What you’ll learn
✅ Importance of preprocessing in the ML pipeline
✅ Data cleaning:
Handling missing values (deletion, imputation)
Detecting and treating outliers
Removing duplicates & noise
✅ Data transformation:
Scaling and normalization
Encoding categorical variables (label vs one-hot encoding)
Feature scaling for algorithms like KNN, SVM, and regression
✅ Feature engineering:
Creating new features from existing data
Polynomial features and interaction terms
Domain-specific feature creation
✅ Best practices for preparing data before model training

🧪 Why this matters
👉 Clean data → Better models.
Without proper preprocessing, even the best ML models fail. Understanding this pipeline ensures your algorithms are efficient, accurate, and robust.

📘 Next up
Next video: Data Cleaning Project: Handling Missing Values and Outliers
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