Search Results for "statistical"
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Showing 43 open source projects for "statistical"
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Skillfully - The future of skills based hiringSkillfully transforms hiring through AI-powered skill simulations that show you how candidates actually perform before you hire them. Our platform helps companies cut through AI-generated resumes and rehearsed interviews by validating real capabilities in action. Through dynamic job specific simulations and skill-based assessments, companies like Bloomberg and McKinsey have cut screening time by 50% while dramatically improving hire quality.
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Regpack: All-in-One Online Registration and Payment SoftwareRegpack is a powerful onboarding, registration, and payments platform trusted by thousands of organizations worldwide. Our mission is simple: to give you the tools to automate busywork, streamline your processes, and keep your focus where it belongs, on growing your programs and serving your clients.
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Synthetic Data Generator
SDG is a specialized framework
...The system supports multiple generation methods including statistical models, generative adversarial networks, and large language model–based synthesis. It also includes a data processing module capable of handling different data types, preprocessing columns, managing missing values, and converting formats automatically before model training. -
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spaCy
Industrial-strength Natural Language Processing (NLP)
spaCy is a library built on the very latest research for advanced Natural Language Processing (NLP) in Python and Cython. Since its inception it was designed to be used for real world applications-- for building real products and gathering real insights. It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with an accuracy within 1% of the best available. It's blazing fast, easy to install and comes with a simple and productive API. -
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pmdarima
Statistical library designed to fill the void in Python's time series
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function. -
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StatsForecast
Fast forecasting with statistical and econometric models
StatsForecast is a Python library for time-series forecasting that delivers a suite of classical statistical and econometric forecasting models optimized for high performance and scalability. It is designed not just for academic experiments but for production-level time-series forecasting, meaning it handles forecasting for many series at once, efficiently, reliably, and with minimal overhead. The library implements a broad set of models, including AutoARIMA, ETS, CES, Theta, plus a battery of benchmarking and baseline methods, giving users flexibility in selecting forecasting approaches depending on data characteristics (trend, seasonality, intermittent demand, etc.). ... -
ToogleBox: Simplify, Automate and Improve Google Workspace FunctionalitiesToogleBox was created as a solution to address the challenges faced by Google Workspace Super Admins. We developed a premium and secure Software-as-a-Service (SaaS) product completely based on specific customer needs. ToogleBox automates most of the manual processes when working with Google Workspace functionalities and includes additional features to improve the administrator experience.
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NVIDIA Earth2Studio
Open-source deep-learning framework
...The toolkit makes it easy to run deterministic and ensemble forecasts, swap models interchangeably, and process large geophysical datasets with Xarray structures, enabling experimentation with state-of-the-art deep learning models for climate and atmospheric prediction. Users can extend Earth2Studio with optional model packs, advanced data interfaces, statistical operators, and backend integrations that support flexible workflows from simple tests to large-scale operational inference. -
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PaperBanana
Extension of Google Research’s PaperBanana
PaperBanana is an open-source agentic framework designed to automatically generate publication-quality academic diagrams and statistical plots directly from text descriptions. The project focuses on helping researchers, educators, and data scientists transform conceptual descriptions of figures into structured visual outputs suitable for research papers, presentations, and technical reports. Instead of manually designing charts or diagrams using traditional visualization tools, users can describe the desired figure in natural language and allow the system to generate the visual representation automatically. ... -
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Natural Language Toolkit
NLTK Source
The Natural Language Toolkit (NLTK) is a widely used open-source Python library designed for working with human language data and building natural language processing (NLP) applications. It provides a comprehensive suite of modules, datasets, and tutorials that support both symbolic and statistical approaches to language processing. The toolkit includes implementations of many foundational NLP algorithms and utilities, enabling developers to perform tasks such as tokenization, stemming, parsing, classification, and semantic reasoning. NLTK was originally developed to support research and teaching in computational linguistics and artificial intelligence, and it has become one of the most influential educational platforms for learning NLP in Python. ... -
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MiniSom
MiniSom is a minimalistic implementation of the Self Organizing Maps
MiniSom is a minimalistic and Numpy-based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial Neural Network able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. Minisom is designed to allow researchers to easily build on top of it and to give students the ability to quickly grasp its details. The project initially aimed for a minimalistic implementation of the Self-Organizing Map (SOM) algorithm, focusing on simplicity in features, dependencies, and code style. ... -
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TensorFlow Probability
Probabilistic reasoning and statistical analysis in TensorFlow
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. -
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Potpie
Create custom engineering agents for your codebase
Potpie is an AI-powered data analysis tool that automates the exploration and visualization of datasets, assisting users in uncovering insights without extensive coding. -
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NBA Sports Betting Machine Learning
NBA sports betting using machine learning
...Machine learning models are then trained to estimate the probability that a team will win a game as well as whether the total score will fall above or below the sportsbook’s predicted total. In addition to predicting outcomes, the project evaluates expected value to determine whether a potential bet offers a statistical advantage compared with sportsbook odds. -
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DataProfiler
Extract schema, statistics and entities from datasets
DataProfiler is an AI-powered tool for automatic data analysis and profiling, designed to detect patterns, anomalies, and schema inconsistencies in structured and unstructured datasets. The DataProfiler is a Python library designed to make data analysis, monitoring, and sensitive data detection easy. Loading Data with a single command, the library automatically formats & loads files into a DataFrame. Profiling the Data, the library identifies the schema, statistics, entities (PII / NPI), and... -
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
...The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run them in a sandbox environment, and analyze the results with statistical methods. The platform also uses multi-agent debate and automated peer review processes to refine research findings and improve paper quality. By combining literature discovery, experimentation, and writing automation, AutoResearchClaw aims to turn research ideas into conference-ready papers with minimal human intervention. -
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NeuralForecast
Scalable and user friendly neural forecasting algorithms.
...Unfortunately, available implementations and published research are yet to realize neural networks' potential. They are hard to use and continuously fail to improve over statistical methods while being computationally prohibitive. For this reason, we created NeuralForecast, a library favoring proven accurate and efficient models focusing on their usability. -
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Quantitative Trading System
A comprehensive quantitative trading system with AI-powered analysis
...The project is designed to provide an end-to-end infrastructure for building and operating algorithmic trading strategies in financial markets. It includes tools for collecting and processing market data from multiple sources, performing statistical and machine learning analysis, and generating trading signals based on quantitative models. The system supports real-time data streaming, allowing strategies to respond to market conditions as they evolve. QuantMuse also incorporates advanced risk management features, including portfolio monitoring, risk limits, and dynamic position sizing to control exposure. -
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Synthetic Data Vault (SDV)
Synthetic Data Generation for tabular, relational and time series data
The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent software systems without the risk of exposure that comes with data disclosure. Underneath the hood it uses several probabilistic graphical modeling and deep learning based techniques. ... -
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mlforecast
Scalable machine learning for time series forecasting
mlforecast is a time-series forecasting framework built around machine-learning models, designed to make forecasting both efficient and scalable. It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates... -
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BettaFish
Public opinion analysis system
...It uses a modular architecture of specialized agents that collaborate to crawl mainstream platforms, extract multimodal content like text and short video, and synthesize insights through both statistical and large language model techniques. With a design that lets users pose questions in natural language and receive structured reports, charts, and visualizations, the system aims to break information cocoons and provide comprehensive views of trends and public sentiment. Unlike simpler analytics tools, BettaFish employs agent collaboration and a “forum” style internal mechanism to combine diverse model outputs, making the analysis richer and more robust. ... -
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Lingua-Py
The most accurate natural language detection library for Python
Its task is simple: It tells you which language some text is written in. This is very useful as a preprocessing step for linguistic data in natural language processing applications such as text classification and spell checking. Other use cases, for instance, might include routing e-mails to the right geographically located customer service department, based on the e-mails' languages. Language detection is often done as part of large machine learning frameworks or natural language processing... -
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AutoViz
Automatically Visualize any dataset, any size
...The system also includes built-in tools for evaluating data quality and identifying potential issues such as missing values or unusual distributions. By automating the visualization process, AutoViz allows users to rapidly explore datasets before applying machine learning models or statistical analysis. -
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YData Synthetic
Synthetic data generators for tabular and time-series data
A package to generate synthetic tabular and time-series data leveraging state-of-the-art generative models. Synthetic data is artificially generated data that is not collected from real-world events. It replicates the statistical components of real data without containing any identifiable information, ensuring individuals' privacy. This repository contains material related to Generative Adversarial Networks for synthetic data generation, in particular regular tabular data and time-series. It consists a set of different GANs architectures developed using Tensorflow 2.0. ... -
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AI Marketing Skills
Open-source AI marketing skills for Claude Code
AI Marketing Skills is a comprehensive open-source framework designed to transform AI agents into fully operational marketing and sales systems by equipping them with structured, reusable “skills” that automate real business workflows. Instead of simple prompts, the project provides complete operational modules that include scripts, scoring systems, and decision-making logic, allowing AI tools like Claude Code to execute complex marketing tasks end-to-end. The system is organized into... -
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Darts
A python library for easy manipulation and forecasting of time series
darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, combine the predictions of several models, and take external data into account. Darts supports both univariate and multivariate time series and models. The ML-based models... -
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SPPAS
SPPAS - the automatic annotation and analyses of speech
...SPPAS is able to produce automatically speech annotations from a recorded speech sound and its orthographic transcription. SPPAS is helpful for the analysis of any annotated data: estimate statistical distributions, make requests, manage files, visualize annotations. SPPAS offers a file converter from/to a wide range of formats: xra, TextGrid, eaf, trs... <https://sppas.org> -
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WhisperJAV
A subtitle generator for Japanese Adult Videos.
...Transformer-based ASR architectures like Whisper suffer significant performance degradation when applied to the spontaneous and noisy domain of JAV. This degradation is driven by specific acoustic and temporal characteristics that defy the statistical distributions of standard training data.
