Data Preparation and Feature Engineering

Intermediate

📊 Importance of High-Quality Data in Machine Learning

High-quality data is fundamental to effective machine learning.
The process begins with:

  1. 📥 Data Collection
  2. 🧹 Preprocessing, including:
    • 🧼 Cleaning
    • ❓ Handling missing values
    • 📏 Normalization
    • 🔤 Encoding categorical variables

🧠 Feature Engineering

Transforming raw data into features that better represent the underlying problem.
Key Techniques:

  • 📐 Scaling features (e.g., Min-Max, Standardization)
  • 🔗 Creating interaction terms
  • 🕒 Extracting date/time components
  • 🏷️ Encoding categorical variables:
    • 🔢 One-hot encoding
    • 📶 Ordinal encoding

💡 Insight:
Proper feature engineering often outperforms complex algorithms, making it a critical skill in ML.

🍽️ Analogy

Preparing data is like preparing ingredients before cooking —
the quality of input directly affects the final taste of the dish. 👨‍🍳