#Statsmodels
Showing 21 of 21 repositories tagged #statsmodels, ranked by stars
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.
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
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.
General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python
Horizontal Pod Autoscaler built with predictive abilities using statistical models
Time Series Analysis and Forecasting in Python
A library that unifies the API for most commonly used libraries and modeling techniques for time-series forecasting in the Python ecosystem.
Python port of "Common statistical tests are linear models" by Jonas Kristoffer Lindeløv.
Here I go through the processing of prototyping a mean reversion trading strategy using statistical concepts, then test it in backtrader.
Practical financial data science examples applying statistics, time series analysis, graph analytics, backtesting, machine learning, natural language processing, neural networks and LLMs
Sharing the solved Exercises & Project of Statistics for Data Science using Python course on Coursera by Ankit Gupta
Support financial data science workflow, manage large structured and unstructured data sets, and apply financial econometrics and machine learning
On this repository you'll find tools used for Quantitative Analysis and some examples such: MonteCarlo Simulations, Linear Regression, General Data Visualiztions, Time-Series Analysis, etc.
Material for the tutorial, "Time series analysis with pandas" at T-Academy
Undergraduate thesis, Seoul National University Dept. of Economics — "Modeling Volatility and Risk Spillover Between the Financial Markets of US and China Using GARCH Value-at-Risk Forecasting and Granger Causality."
Exercícios do curso "Profissao: Cientista de Dados", sendo realizado pela EBAC - Escola Britânica de Artes Criativas e Tecnologia.
End To End Tutorial on Time Series Analysis and Forcasting
The aim of this project is to develop a solution using Data science and machine learning to predict the compressive strength of a concrete with respect to the its age and the quantity of ingredients used.
Gold Analysis, Trading Strategy, Backtesting, Prediction, and Communication (Email and Telegram notification chatbot) in Python
The collection of exercises I did during Ironhack's Data Science bootcamp.