Search Results for "statistics"
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Showing 255 open source projects for "statistics"
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Book5_Essentials-Probability-Statistics
The book 5 of statistics in simplicity
...The material connects probability theory directly to real analytical workflows, helping learners understand how statistics supports predictive modeling. Like the other books in the series, it blends mathematical explanation with Python-based experimentation. Overall, the project provides a practical statistical foundation for students advancing into AI and data science. -
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SOFA is a statistics, analysis, and reporting program with an emphasis on ease of use, learn as you go, and beautiful output.
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Tautulli
A Python based monitoring and tracking tool for Plex Media Server
Tautulli is a 3rd party application that you can run alongside your Plex Media Server to monitor activity and track various statistics. Most importantly, these statistics include what has been watched, who watched it, when and where they watched it, and how it was watched. The only thing missing is "why they watched it", but who am I to question your 42 plays of Frozen. All statistics are presented in a nice and clean interface with many tables and graphs, which makes it easy to brag about your server to everyone else. -
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Glances
An eye on your system
...Depending on the size of the user interface, this information can then dynamically adapt. Glances can work in client/server mode, and is also capable of remote monitoring. All systems statistics can be exported to files or external time/value databases. Glances gets information from your system through various libraries, and is based on an open architecture, so you can add new plugins or export modules. -
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PyMC
Bayesian Modeling and Probabilistic Programming in Python
PyMC is a Python library for probabilistic programming focused on Bayesian statistical modeling and machine learning. Built on top of computational tools like Aesara and NumPy, PyMC allows users to define models using intuitive syntax and perform inference using MCMC, variational inference, and other advanced algorithms. It’s widely used in scientific research, data science, and decision modeling. -
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Panda-Helper
Panda-Helper: Data profiling utility for Pandas DataFrames and Series
Panda-Helper is a simple data-profiling utility for Pandas DataFrames and Series. Assess data quality and usefulness with minimal effort. Quickly perform initial data exploration, so you can move on to more in-depth analysis. -
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Agent360
360 monitoring agent
360 Monitoring is a web service that monitors and displays statistics of your server performance. Agent360 is OS-agnostic software compatible with Python 3.7 and 3.8. It's been optimized to have low CPU consumption and comes with an extendable set of useful plugins. -
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dxy-covid-19-crawler
Realtime crawler for COVID-19 outbreak statistics from DXY data
DXY-COVID-19-Crawler is a Python-based project designed to collect real-time COVID-19 infection data from the public dataset provided by Ding Xiang Yuan (DXY). The crawler periodically retrieves pandemic statistics and stores them in a database so that historical changes in the outbreak can be preserved and analyzed later. It was created to make up-to-date infection data more accessible for developers, researchers, and analysts who wanted to build visualizations or conduct data analysis during the early stages of the pandemic. DXY-COVID-19-Crawler automatically crawls data at regular intervals, typically every minute, ensuring that newly published statistics are captured as quickly as possible. ... -
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ClusterFuzz
Scalable fuzzing infrastructure
...Monorail, Jira). Supports multiple coverage guided fuzzing engines (libFuzzer, AFL, AFL++ and Honggfuzz) for optimal results (with ensemble fuzzing and fuzzing strategies). Statistics for analyzing fuzzer performance, and crash rates. Easy to use web interface for management and viewing crashes. Support for various authentication providers using Firebase. -
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statsmodels
Statsmodels, statistical modeling and econometrics in Python
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all of the one-parameter exponential family distributions. Markov switching models (MSAR), also known as Hidden Markov Models (HMM). ... -
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X4G
Fast and modern gateway for VLESS tunneling on WebSocket and XHTTP
X4G is a self-hosted gateway for operating VLESS tunnels over WebSocket and two XHTTP transport modes. It also includes an internal HTTP proxy for clients that need a conventional proxy endpoint. A web dashboard displays service statistics, hourly traffic charts, live connections, activity records, and error logs. Administrators can create unlimited connection links with individual traffic quotas, bandwidth limits, expiration dates, ports, and simultaneous IP restrictions. Subscription groups combine multiple configurations into shareable public pages that can be protected with passwords and exported as QR codes. ... -
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Datumaro
Dataset Management Framework, a Python library and a CLI tool to build
...Datumaro makes it easy to merge datasets, split them into training/validation/test subsets, filter or transform annotations, and validate annotation quality — all while preserving metadata and supporting detailed statistics. It’s especially useful when you’re dealing with heterogeneous data sources or need to prepare complex datasets for machine learning workflows, freeing you from writing custom scripts for every format conversion. -
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Toutatis
Extract public Instagram account information from usernames
Toutatis is an open source command-line tool designed to extract publicly available information from Instagram accounts. It helps users gather various data points from a target profile by querying Instagram using a username or account ID. The tool can retrieve details such as profile metadata, follower counts, biography information, and other publicly accessible account attributes. In addition to basic profile data, Toutatis can also reveal contact details that may be publicly exposed,... -
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pandas
Fast, flexible and powerful Python data analysis toolkit
pandas is a Python data analysis library that provides high-performance, user friendly data structures and data analysis tools for the Python programming language. It enables you to carry out entire data analysis workflows in Python without having to switch to a more domain specific language. With pandas, performance, productivity and collaboration in doing data analysis in Python can significantly increase. pandas is continuously being developed to be a fundamental high-level building... -
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Optopsy
A nimble options backtesting library for Python
...As such, we just need to call the long_calls() function to have Optopsy generate all combinations of a simple long call strategy for the specified time period and return a DataFrame. Here we also use Panda's round() function afterwards to return statistics within two decimal places. -
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DataProfiler
Extract schema, statistics and entities from 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 more. Data Profiles can then be used in downstream applications or reports. -
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NBA Sports Betting Machine Learning
NBA sports betting using machine learning
NBA-Machine-Learning-Sports-Betting is an open-source Python project that applies machine learning techniques to predict outcomes of National Basketball Association games for analytical and betting-related research. The system gathers historical team statistics and game data spanning multiple seasons, beginning with the 2007–2008 NBA season and continuing through the present. Using this dataset, the project constructs matchup features that represent team performance trends and contextual information about each game. 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. ... -
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git-extra-commands
A collection of git utilities, useful extra git scripts, tutorials
...The collection includes tools for staging, amending, inspecting history, cleaning branches, resolving conflicts, and working with remotes. Other commands expose repository statistics, large files, ignored files, changed functions, default branches, and repository roots. Several helpers integrate with fzf for interactive commit, reflog, file, and reference browsing. The repository also collects useful Git aliases, installation methods, external utilities, articles, and practical configuration tips. -
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MNE-Python
Magnetoencephalography (MEG) and Electroencephalography EEG in Python
...MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more. -
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ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
ThinkStats2 is the code and text companion for the second edition of Think Stats, an introduction to statistics and data science for Python programmers. It teaches probability and statistical reasoning through short programs, experiments, and analysis of real datasets. The material emphasizes exploratory methods that help readers ask and answer practical questions with data. Case studies draw from public sources, including health-related datasets, to connect abstract concepts with realistic analysis. ... -
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cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio
Cracking the Data Science Interview is an open educational repository that collects study materials, resources, and reference links for preparing for data science interviews. The project organizes content across many fundamental areas of data science, including statistics, probability, SQL, machine learning, and deep learning. It includes cheat sheets that summarize important technical concepts commonly discussed during technical interviews. The repository also provides links to recommended books, tutorials, practice platforms, and blog posts that help learners strengthen their theoretical and practical skills. ... -
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Dispatcharr
Your Ultimate IPTV & Stream Management Companion
...It lets you import M3U/EPG sources, curate and organize them with filtering and grouping, and serve them reliably to clients like Plex, Jellyfin, and Emby with advanced proxying and failover support. Designed with both simple personal use and larger self-hosted setups in mind, Dispatcharr offers real-time streaming statistics, automated EPG matching, and a responsive interface that helps you manage your video on demand (VOD) content alongside live channels. The project has evolved from a personal tool to a collaborative open-source effort with beta-phase features including a dashboard, flexible proxy engine, and compatibility with multiple output formats. ... -
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Xiaomi Gateway 3
Home Assistant custom component for control Xiaomi Multimode Gateway
...The integration works by enabling local access and running gateway-side support logic so Home Assistant can communicate with Zigbee, BLE, and gateway features. It can expose connected Xiaomi ecosystem devices, handle gateway controls, work with device statistics, and support advanced configuration. It also includes modes for Zigbee Home Automation, Zigbee2MQTT, and custom Zigbee firmware workflows. XiaomiGateway3 is best for Home Assistant users who want local gateway integration without replacing supported Xiaomi gateway firmware. -
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Data Science Interviews
Data science interview questions and answers
...The repository organizes questions into different categories including theoretical machine learning concepts, technical programming questions, and probability or statistics problems. Many of the questions cover fundamental machine learning topics such as linear models, decision trees, neural networks, and evaluation metrics. In addition to theoretical questions, the repository also includes practical interview topics related to coding challenges, SQL queries, and algorithmic thinking. -
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whylogs
The open standard for data logging
...With whylogs, users are able to generate summaries of their datasets (called whylogs profiles) which they can use to track changes in their dataset Create data constraints to know whether their data looks the way it should. Quickly visualize key summary statistics about their datasets. whylogs profiles are the core of the whylogs library. They capture key statistical properties of data, such as the distribution (far beyond simple mean, median, and standard deviation measures), the number of missing values, and a wide range of configurable custom metrics. By capturing these summary statistics, we are able to accurately represent the data and enable all of the use cases described in the introduction.
