numynal 0.1.1

A comprehensive data anlysis library

numynal

A comprehensive data analysis library

Modules

• Data preprocessing

  • Missing data
  • Data normalisation and scaling
  • Data augmentation and feature engineering
  • Outlier detection and handling
  • Data imputation

• EDA

  • Statistical summary
  • Correlation and Covariance analysis
  • Interactive web dashboards

• Statstical analysis

  • Distribution
  • Statistical model building
  • Confidence intervals and bootstrap
  • Hypothesis testing
  • Bayesian Methods

• Time Series analysis

  • Decomposition
  • Forecasting methods
  • Anomoly detection

• Autograd

  • Building up on pyAutoGrad
  • Custom optimisation models
  • Autodiff graph visualisation

• ML Models

  • Supervised learning
  • Unsupervised learning
  • Ensemble methods
  • Hyperparameter tuning and model evaluation
  • Model pipelines
  • Transfer learning

• Deep learning

  • NN modules
  • Graph NN
  • Quantisation and pruning
  • pretrained models for common tasks
  • custom architecture support
  • optimisation techniques

• Visualisation

  • Traditional visualisation
  • Model performance visualisation

• Optimisation

  • Parallel processing and GPU support
  • AutoML
  • Model explainability and interpretability support
  • Workflow automation
  • Distributed training

• Performance Monitoring

  • Metrics tracking
  • Real-time monitoring for deployed model
  • Feedback loop

• API support to databases

  • SQL databases (postgres, sqlite, mysql)
  • MongoDB
  • Data streaming support