2. Handling Missing Data

Missing values are inevitable. Here’s how to manage them:

• Detection: Look for NaNs, None, empty strings, or placeholders like -999
• Imputation Strategies:
• Mean, median, or mode
• Forward/backward fill
• KNN or predictive models
• Add binary indicators for missingness
• Deletion: Remove rows or columns if data is missing beyond a threshold

3. Data Type Conversion

Ensure every column has the correct format:

• Strings to numbers (e.g., ‘1,000’ to 1000)
• Datetime parsing
• Boolean normalization (‘Yes’/’No’ to True/False)
• Category optimization for memory

4. Outlier Detection and Treatment

Outliers can distort analysis. Use these methods:

• Detection: Z-score, IQR, boxplots, scatter plots
• Treatment: Remove, cap, or impute; use winsorization if needed

5. Encoding Categorical Variables

Most algorithms can’t handle raw categorical data:

• Label Encoding (Ordinal)
• One-Hot Encoding
• Target Encoding
• Frequency or Hash Encoding (for high cardinality)

6. Text Cleaning (NLP Preprocessing)

For text data:

• Lowercasing and trimming spaces
• Remove punctuation, HTML tags, emojis
• Tokenization and stopword removal
• Stemming or lemmatization
• Spelling correction

7. Scaling and Normalization

Standardize feature values:

• Techniques:
• Min-Max Scaling
• Z-score Standardization
• Robust Scaling
• Log or Power Transform (Box-Cox, Yeo-Johnson)

8. Feature Engineering

Create new insights from raw data:

• Extract datetime features (day, month, etc.)
• Generate text length or ratios (e.g., price per unit)
• Group-wise aggregations
• Polynomial and interaction terms
• Binning/discretization

9. Dimensionality Reduction

Reduce feature space while retaining information:

• PCA (Principal Component Analysis)
• t-SNE / UMAP (for visualization)
• Remove multicollinearity (VIF)
• Feature selection by correlation or importance

10. Date and Time Data Preprocessing

Handle time-based data smartly:

• Convert to datetime format
• Extract components (day, month, weekday)
• Calculate time differences (duration)
• Adjust time zones
• Detect trends and seasonality

11. Handling Duplicates and Redundancies

Avoid repeated or unnecessary data:

• Drop duplicates
• Identify constant or quasi-constant columns
• Consolidate similar records

12. Data Consistency and Standardization

Ensure uniformity:

• Standardize units and naming conventions
• Correct typos
• Use regex for pattern matching

13. Data Integration and Merging

Combine multiple sources:

• Joins (inner, left, right, full)
• Concatenations
• Resolve schema mismatches
• Handle conflicting keys or data

14. Imbalanced Classes (for Classification Problems)

Balance class distribution:

• Oversampling (SMOTE, ADASYN)
• Undersampling
• Class weighting
• Stratified sampling

15. Data Sampling and Partitioning

Prepare for training and validation:

• Random or stratified sampling
• Train/validation/test split
• Cross-validation
• Bootstrapping

16. Data Anonymization and Privacy

For sensitive datasets:

• Masking personal identifiers
• Tokenization
• Generalization
• Differential privacy (advanced)

17. Saving and Exporting Cleaned Data

Preserve your preprocessed work:

• Save in CSV, JSON, Excel, Parquet, or SQL
• Compress and version datasets
• Ensure schema consistency

18. Automating with Data Pipelines

Build reproducible workflows:

• Use tools like Pandas pipelines, Scikit-learn Pipelines, KNIME, Airflow
• Parameterize steps
• Log transformations and decisions

Bonus: Data Preprocessing Special Types

• Image: Resizing, normalization, augmentation
• Audio: Denoising, MFCC extraction, resampling
• Geospatial: Coordinate parsing, geocoding, distance calculations