Data preprocessing is a critical step in the machine learning pipeline. It ensures your dataset is clean, consistent, and ready for modeling. Here's a breakdown of key techniques:

1. Data Cleaning 🧹

  • Handle Missing Values: Use interpolation, mean imputation, or drop rows/columns.
  • Remove Outliers: Apply Z-score or IQR methods.
  • Correct Errors: Standardize formats (e.g., dates, units).
Data Cleaning Overview

2. Data Transformation 🔄

  • Encoding Categorical Variables: One-hot encoding or label encoding.
  • Log Transformation: To normalize skewed distributions.
  • Binning: Group continuous values into discrete intervals.

3. Data Normalization ⚖️

  • Min-Max Scaling: Rescale values to [0, 1].
  • Z-Score Normalization: Standardize data with mean 0 and std 1.
  • Robust Scaling: Use median and IQR for robustness.
Data Normalization Methods

4. Data Splitting 📊

  • Train/Validation/Test Split: Allocate data for training, tuning, and evaluation.
  • Cross-Validation: Techniques like k-fold for robust model assessment.

For deeper insights into data visualization after preprocessing, check out our Data Visualization Introduction.

5. Key Considerations ⚠️

  • Always validate data quality before modeling.
  • Choose normalization methods based on algorithm requirements.
  • Document preprocessing steps for reproducibility.
Data Preprocessing Flowchart