Data preprocessing is a critical step in the machine learning pipeline. It ensures data quality and prepares datasets for effective modeling. Here are key techniques:

1. Data Cleaning 🧹

  • Remove duplicates: Use drop_duplicates() in pandas.
  • Handle missing values: Impute with mean/median or remove rows/columns.
  • Correct inconsistencies: Standardize formats (e.g., "New York" → "New_York").
Data Cleaning Process

2. Feature Engineering 🛠️

  • Create new features: Derive insights from existing data (e.g., "age_group" from numerical age).
  • Encode categorical variables: Use one-hot encoding or label encoding.
  • Normalize/Standardize: Scale features to a common range or distribution.
Feature Engineering Steps

3. Data Splitting 📊

  • Split data into training, validation, and test sets (e.g., 70-15-15 ratio).
  • Use libraries like scikit-learn for stratified splitting.
  • Always validate your split strategy with /feature-validation-methods.

4. Outlier Detection 🔍

  • Identify outliers using Z-score, IQR, or visualization tools.
  • Decide to remove, cap, or transform them based on domain knowledge.
Outlier Detection Example

For deeper exploration, check our guide on data standardization. Let me know if you need examples in code!