Skip to content

Latest commit

 

History

History
269 lines (200 loc) · 7.41 KB

ML_Modelling_Considerations.md

File metadata and controls

269 lines (200 loc) · 7.41 KB
Raw

Machine Learning Modeling Pipeline

1. Initial Set-Up & Import Data

  • Define project goals and objectives
  • Set up development environment (e.g., Jupyter Notebook, PyCharm, VS Code)
  • Install necessary libraries and dependencies
  • Import required packages
  • Load datasets (CSV, SQL, API, web scraping, etc.)
  • Set random seeds for reproducibility

2. Exploratory Data Analysis (EDA)

2.1 Initial Data Inspection

  • Dataset shape and size
  • Data types (numeric, categorical, datetime, etc.)
  • Missing data analysis
  • Duplicate records identification
  • Basic statistical summary (mean, median, mode, std dev, etc.)
  • Data distributions (histograms, box plots, violin plots)

2.2 Data Visualization

  • Correlation heatmaps
  • Pair plots
  • Bar plots
  • Scatter plots
  • Line plots for time series data
  • Geographical plots if applicable

2.3 Data Cleaning Methods

  • Handle missing values
    • Simple imputation (mean, median, mode, random)
    • Complex imputation (regression, KNN, MICE)
    • Time series specific (forward fill, backward fill, interpolation)
  • Handle outliers
    • Z-score method
    • IQR method
    • DBSCAN clustering
  • Fix inconsistent data entries
  • Correct data types
  • Handle duplicate records

2.4 Advanced Analysis

  • Time series decomposition (for time series data)
  • Seasonality and trend analysis
  • Customer segmentation (if applicable)
  • Anomaly detection
  • Text data analysis (if applicable)
    • Word clouds
    • N-gram analysis
    • Topic modeling

3. Feature Engineering & Feature Selection

3.1 Feature Creation

  • Domain-specific feature engineering
  • Interaction terms
  • Polynomial features
  • Date-time features (day of week, month, year, etc.)
  • Text-based features (TF-IDF, word embeddings)
  • Aggregations (mean, sum, count by group)
  • Lag features for time series
  • Window-based features (rolling statistics)

3.2 Feature Transformation

  • Log transformation
  • Box-Cox transformation
  • Yeo-Johnson transformation
  • Binning (equal-width, equal-frequency)

3.3 Encoding Categorical Variables

  • One-hot encoding
  • Label encoding
  • Target encoding
  • Frequency encoding
  • Binary encoding
  • Embedding (for high cardinality)

3.4 Feature Selection Techniques

  • Correlation analysis (Pearson, Spearman, Kendall)
  • Variance threshold
  • Univariate feature selection (chi-squared, ANOVA F-value)
  • Recursive Feature Elimination (RFE)
  • Feature importance from tree-based models
  • L1 regularization (Lasso)
  • Mutual Information
  • Boruta algorithm

3.5 Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • t-SNE
  • UMAP
  • Autoencoders
  • Factor Analysis

4. Data Pre-processing

4.1 Scaling and Normalization

  • Standardization (Z-score normalization)
  • Min-Max scaling
  • Robust scaling
  • Normalization (L1, L2)

4.2 Handling Class Imbalance

  • Oversampling techniques (Random, SMOTE, ADASYN)
  • Undersampling techniques (Random, Tomek links, NearMiss)
  • Combination methods (SMOTETomek, SMOTEENN)
  • Class weights adjustment
  • Ensemble methods for imbalanced data

4.3 Data Augmentation

  • Synthetic data generation (GANs, VAEs)
  • Data augmentation for images (rotation, flipping, zooming)
  • Data augmentation for text (back-translation, synonym replacement)

5. Defining Metrics & Baseline Models

5.1 Evaluation Metrics

  • Classification metrics (accuracy, precision, recall, F1-score, ROC-AUC, PR-AUC)
  • Regression metrics (MSE, RMSE, MAE, R-squared, MAPE)
  • Ranking metrics (NDCG, MAP)
  • Custom loss functions

5.2 Baseline Models

  • Simple heuristics (e.g., majority class, mean prediction)
  • Linear models (Logistic Regression, Linear Regression)
  • Decision Trees
  • Simple ensemble methods (Random Forest, Gradient Boosting)

6. Model Training & Evaluation

6.1 Model Selection

  • Linear models (Ridge, Lasso, Elastic Net)
  • Tree-based models (Decision Trees, Random Forests, Gradient Boosting Machines)
  • Support Vector Machines
  • K-Nearest Neighbors
  • Neural Networks (MLPs, CNNs, RNNs, Transformers)
  • Ensemble methods (Bagging, Boosting, Stacking)

6.2 Cross-Validation Strategies

  • K-Fold cross-validation
  • Stratified K-Fold
  • Time series cross-validation
  • Leave-one-out cross-validation

6.3 Hyperparameter Optimization

  • Grid Search
  • Random Search
  • Bayesian Optimization
  • Genetic Algorithms
  • Optuna, Hyperopt

6.4 Model Interpretation

  • Feature importance
  • SHAP (SHapley Additive exPlanations) values
  • LIME (Local Interpretable Model-agnostic Explanations)
  • Partial Dependence Plots
  • Individual Conditional Expectation plots

7. Final Model & Results

  • Model selection based on performance metrics
  • Ensemble methods (if applicable)
  • Final model training on full dataset
  • Model serialization (saving the model)
  • Performance report generation
  • Visualization of results

8. Deployment & Monitoring

8.1 Model Deployment Strategies

Web Servers / APIs

  • How it works: The model is deployed on a traditional web server (e.g., Flask, Django for Python) and exposed via an API.
  • Key points:
    • Suitable for models with consistent, moderate traffic
    • Allows fine-grained control over the server environment
    • Requires manual scaling and maintenance
    • Examples: REST API using Flask, FastAPI

Serverless

  • How it works: The model is deployed as a function that runs on-demand in a cloud provider's serverless infrastructure.
  • Key points:
    • Ideal for sporadic or unpredictable traffic patterns
    • Automatic scaling and pay-per-use pricing
    • Cold start latency can be an issue for some use cases
    • Limited execution time and memory
    • Examples: AWS Lambda, Azure Functions, Google Cloud Functions

Container Orchestration

  • How it works: The model is packaged in a container (e.g., Docker) and deployed on a container orchestration platform.
  • Key points:
    • Suitable for complex models or those requiring specific environments
    • Provides portability and consistency across different environments
    • Allows for easy scaling and management of multiple model versions
    • Requires more setup and management compared to serverless
    • Examples: Kubernetes, Amazon ECS, Azure Kubernetes Service

8.2 Deployment Process

  • Model packaging (serialization)
  • Environment configuration
  • Deployment script creation
  • Testing in staging environment
  • Gradual rollout (e.g., canary deployment)

8.3 Monitoring and Logging

  • Set up monitoring for model performance
  • Implement logging for predictions and errors
  • Create dashboards for key metrics
  • Set up alerts for anomalies or performance degradation

8.4 Scaling and Load Balancing

  • Implement auto-scaling based on traffic patterns
  • Set up load balancing for high-availability

8.5 Security Considerations

  • Implement authentication and authorization
  • Encrypt data in transit and at rest
  • Regular security audits and updates

8.6 Continuous Integration/Continuous Deployment (CI/CD)

  • Automate the deployment process
  • Implement version control for models and code
  • Set up automated testing before deployment

8.7 A/B Testing Framework

  • Implement infrastructure for comparing model versions
  • Set up metrics collection for performance comparison

8.8 Model Versioning and Rollback

  • Maintain version history of deployed models
  • Implement quick rollback mechanisms for issues

9. Documentation & Reporting

  • Code documentation
  • Jupyter notebook with analysis and results
  • Technical report
  • Non-technical executive summary
  • Presentation of findings