Module 3: Data Preprocessing in Python

Topics Covered

• Data Cleaning

  Data cleaning involves handling missing values, removing duplicates, and correcting inconsistencies:

  • Handling missing data using methods like mean, median, or mode imputation
  • Removing or filling duplicate rows
  • Correcting errors in data entry
• Feature Scaling

  Feature scaling ensures that features are on the same scale, which improves model performance:

  • Normalization: Scaling features to a range (0, 1)
  • Standardization: Converting features to have zero mean and unit variance
• Encoding Categorical Data

  Categorical data can be encoded into numerical values for model processing:

  • One-Hot Encoding
  • Label Encoding