Data Preprocessing Checklist for Classification ML Model

1. Understand the Data:
   • Review the dataset and understand the problem you’re solving.
   • Identify the features (independent variables) and target (dependent variable).
   • Check the type of problem (binary or multi-class classification) and the distribution of the target variable.
2. Handle Missing Data:
   • Missing Features: Check for any missing values in the features and decide whether to:
     • Impute missing values (e.g., using mean, median, mode, or a predictive model).
     • Drop rows/columns with missing data if they are non-essential.
   • Missing Target: Ensure the target variable does not contain missing values; otherwise, remove or impute them.
3. Data Cleaning:
   • Remove Duplicates: Identify and remove duplicate rows to avoid biased model training.
   • Handle Outliers: Identify and address outliers in the features that may skew results. Methods include removing or transforming outliers based on domain knowledge.
   • Correct Inconsistent Data: Standardize the format (e.g., date format, categorical variables) and remove any inconsistencies in the data entries.
4. Encode Categorical Data:
   • Label Encoding: For ordinal features where the order matters, use label encoding (e.g., low=0, medium=1, high=2).
   • One-Hot Encoding: For nominal categorical features where the order does not matter, use one-hot encoding to create binary columns for each category.
   • Handle Rare Categories: Merge rare categories into a single “Other” category if necessary.
5. Feature Scaling:
   • Standardization: Apply standardization (z-score) for models sensitive to feature scaling (e.g., logistic regression, SVM, k-NN). This centers features around 0 with unit variance.
   • Normalization: Apply normalization (min-max scaling) when features need to be scaled to a specific range (0-1).
   • Robust Scaling: For data with outliers, use robust scaling that uses median and interquartile range instead of mean and standard deviation.
6. Feature Engineering:
   • Create New Features: Based on domain knowledge, create new features (e.g., extracting date/time features like day of the week, month).
   • Interaction Terms: Consider creating interaction terms between features if you suspect relationships between them could improve predictive performance.
   • Feature Selection: Remove highly correlated features using methods like correlation matrix or feature importance to avoid multicollinearity and improve model efficiency.
7. Data Splitting:
   • Training and Test Split: Split the dataset into training and testing sets, typically a 70/30 or 80/20 ratio.
   • Validation Split: Consider using a validation set or cross-validation techniques to tune hyperparameters and prevent overfitting.
8. Handle Class Imbalance (If Applicable):
   • Resampling Methods: For imbalanced classes, consider using oversampling (e.g., SMOTE) or undersampling to balance the dataset.
   • Class Weights: For certain models (e.g., decision trees, logistic regression), you can adjust class weights to account for imbalanced classes.
   • Synthetic Data Generation: In some cases, generate synthetic data for underrepresented classes using techniques like SMOTE (Synthetic Minority Over-sampling Technique).
9. Verify Data Integrity:
   • Ensure No Data Leakage: Confirm that no information from the test set has been included in the training process to avoid overfitting and ensure model generalization.
   • Consistency Across Features: Ensure that the feature columns are consistent between training and testing datasets (e.g., identical column names, same data types).
10. Final Data Review:
    • Sanity Check: Perform a final check to ensure all preprocessing steps have been completed correctly and that the dataset is ready for model training.
    • Visual Inspection: If possible, visualize the data (e.g., using histograms or box plots) to confirm that the preprocessing steps were successful and that the data distribution is reasonable.