Data preprocessing is a critical step in the machine learning pipeline. It ensures your dataset is clean, consistent, and ready for analysis. Here are key techniques to master:

🧹 Data Cleaning

  • Remove duplicates: Use tools like drop_duplicates() in pandas.
  • Handle outliers: Apply statistical methods or visualization (e.g., box plots).
  • Correct errors: Validate data entries and fix inconsistencies.
Data_Cleaning

📊 Data Normalization

Normalize data to scale features between 0 and 1. Common methods include:

  • Min-Max Scaling: X = (X - min) / (max - min)
  • Z-Score Normalization: X = (X - μ) / σ
Data_Normalization

🔐 Feature Encoding

Convert categorical variables into numerical formats:

  • One-Hot Encoding: For nominal categories.
  • Label Encoding: For ordinal categories.
  • Binary Encoding: A compromise between the two.
Feature_Encoding

🧠 Handling Missing Values

  • Impute with mean/mode for numerical/categorical data.
  • Delete rows/columns if missing values are excessive.
  • Use advanced methods like KNN imputation.

🔄 Data Transformation

  • Log transformation: To handle skewed distributions.
  • Binning: Group continuous values into discrete intervals.
  • Polynomial features: Create interaction terms for models.
Data_Transformation

For deeper insights, explore our Data Cleaning Tutorial to refine your datasets further. 📚