Some Common Data Preprocessing Techniques Would Be:

• Data Cleaning:
  • Handling Missing Values: Strategies include removing missing values, imputing missing values using mean, median, mode, or more sophisticated methods like K-Nearest Neighbors (KNN) imputation.
  • Handling Outliers: Outliers can be detected and treated by methods such as removing them, transforming them, or using robust statistical techniques.
• Data Integration:
  • Combining Data from Multiple Sources
• Data Transformation:
  • Normalization/Standardization
  • Log Transformation
  • Discretization
• Data Reduction:
  • Dimensionality Reduction
  • Feature Selection
• Data Encoding:
  • One-Hot Encoding: Converting categorical variables into a binary matrix.
  • Label Encoding: Converting categorical labels into numeric form.
• Feature Engineering:
  • Creating New Features: Generating new features based on existing data to enhance model performance.
  • Polynomial Features: Creating polynomial terms to capture non-linear relationships.
• Data Augmentation:
  • Synthetic Data Generation: Creating additional samples using techniques like oversampling (e.g., SMOTE) or under sampling to balance class distribution.
• Handling Imbalanced Data:
  • Resampling Techniques: Using oversampling or undersampling to balance the class distribution.
  • Using Appropriate Metrics
  • Feature Scaling
  • Removing Duplicates