Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python

📜 🎉 Automated reporting of objects in R

Horizontal Pod Autoscaler built with predictive abilities using statistical models

Hierarchical Time Series Forecasting with a familiar API

Nyoka is a Python library that helps to export ML models into PMML (PMML 4.4.1 Standard).

Input Output Hidden Markov Model (IOHMM) in Python

A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.

Time Series Analysis and Forecasting in Python

Python port of "Common statistical tests are linear models" by Jonas Kristoffer Lindeløv.

Time Series Decomposition techniques and random forest algorithm on sales data

Here I go through the processing of prototyping a mean reversion trading strategy using statistical concepts, then test it in backtrader.

Implemented an A/B Testing solution with the help of machine learning

Financial and Investment Data Science: FinDS Python library and examples for applying quantitative and machine learning methods on structured and unstructured financial data sets

Sharing the solved Exercises & Project of Statistics for Data Science using Python course on Coursera by Ankit Gupta

Jupyter notebooks, accompanying the FinDS Python repo: contains code examples and results for 30+ financial data science projects

Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification