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This project is a Python data analysis library and exploratory data analysis framework designed for processing raw datasets. It provides a suite of tools for examining data, identifying anomalies, and applying statistical methods to uncover patterns. The repository functions as a machine learning modeling toolkit and a statistical data modeling suite. It includes predictive algorithms and mathematical models used to analyze relationships between data variables and derive insights from complex datasets. The project covers a broad range of capabilities including data science, machine learning
This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries for numerical analysis, statistics, and mathematical optimization. It serves as a foundational toolkit for developing applications in machine learning, digital signal processing, and computer vision. The framework provides specialized toolkits for training and deploying predictive models, including neural networks, support vector machines, and decision trees. It further distinguishes itself with deep integrations for real-time visual analysis, such as object tracking and facia
This repository serves as an educational resource and structured curriculum for performing statistical analysis using Python. It provides a comprehensive guide to the scientific computing workflow, focusing on the practical application of data cleaning, numerical modeling, and distribution visualization. The tutorial covers the end-to-end process of transforming raw tabular data into actionable insights. It demonstrates how to manipulate structured datasets through merging and aggregation, perform descriptive and inferential statistical calculations, and fit regression models to evaluate rela
This project is a synthetic data generator designed to create realistic tabular and time-series datasets for machine learning and testing workflows. It functions as a privacy-preserving platform that models the underlying statistical distributions of source data to produce new records that maintain the original statistical properties and structural integrity. The tool distinguishes itself by utilizing CPU-optimized statistical sampling, allowing for high-performance data generation on standard hardware without the need for specialized graphics processing units. It employs a configuration-driv
Google's Causal Impact Algorithm Implemented on Top of TensorFlow Probability.
The main features of willianfuks/tfcausalimpact are: Statistical Modeling.
Projects with overlapping indexed features include: willkoehrsen/data-analysis — This project is a Python data analysis library and exploratory data analysis framework designed for processing raw… accord-net/framework — This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries… lazyprogrammer/machine_learning_examples — This project is a comprehensive collection of practical code examples and implementation libraries for machine… data-centric-ai-community/fg-data-synthetic — This project is a synthetic data generator designed to create realistic tabular and time-series datasets for machine… fonnesbeck/statistical-analysis-python-tutorial — This repository serves as an educational resource and structured curriculum for performing statistical analysis using… dswah/pygam — [CONTRIBUTORS WELCOME] Generalized Additive Models in Python.