📘 Advanced Data Preprocessing Techniques

Data preprocessing is a critical step in the machine learning pipeline. Here's a structured guide to mastering advanced methods:

🔍 Key Concepts

• Data Cleaning:
  Remove outliers 🚫 and handle missing values 📉.

  Example: Use interpolation for time-series data or remove rows with nulls.

• Feature Engineering:
  Create meaningful features 🛠️ like polynomial features or interaction terms.

  Tip: Apply domain knowledge to derive new variables (e.g., `Age_Group` from numerical age).

• Data Normalization:
  Scale features to a standard range (e.g., 0-1) using Min-Max or Z-Score normalization.

  Note: Always normalize *after* feature selection to avoid bias.

📚 Further Reading

• Data Visualization Basics for insights on preprocessing visualization
• Model Training Overview to understand how preprocessing impacts model performance

🧠 Practical Tools

• Pandas: df.fillna(), df.interpolate()
• Scikit-learn: StandardScaler, RobustScaler
• NumPy: Array operations for data transformation

By mastering these techniques, you'll unlock better model accuracy! 🚀