Data & Analytics

Time-Series-Forecasting-and-Deep-Learning is a comprehensive, open-source GitHub repository dedicated to curating resources for time series forecasting and deep learning. It serves as a valuable hub for researchers, data scientists, and students seeking to explore the latest advancements in the field. The repository meticulously organizes research papers, including those from 2017 up to 2026, alongside benchmarks, applications like TimeGPT, and various datasets. Additionally, it provides links to relevant courses, blogs, and code libraries, making it an all-in-one reference for anyone involved in time series analysis and model development. The structured content, including a table of contents, allows for easy navigation through a vast collection of academic and practical materials.

tslearn is an open-source machine learning toolkit specifically designed for time series analysis in Python. It provides a wide array of functionalities for tasks such as clustering, classification, and regression of time series data. The toolkit supports various data preprocessing steps, including scaling and resampling, and offers different distance metrics like Dynamic Time Warping (DTW). tslearn is built to be compatible with scikit-learn's API, allowing users to leverage familiar utilities for hyper-parameter tuning and pipelines. It also includes features for calculating barycenters, performing early classification, and working with UCR datasets, making it a versatile tool for researchers and practitioners in the field.

transdim is an open-source machine learning project focused on transportation data imputation and prediction. It provides models to address challenges in spatiotemporal data modeling, specifically dealing with incomplete data and forecasting future traffic states. The project implements various machine learning models, mainly in Python using Numpy and Jupyter Notebooks, for tasks such as missing data imputation (e.g., random, non-random, and blockout missing patterns) and spatiotemporal prediction, both with and without missing values. It supports a range of publicly available transportation datasets, including traffic speed, volume, and passenger flow data from various cities. The project aims to create accurate and efficient solutions for these complex data challenges, offering practical examples and documentation for implementation and evaluation.

This project implements a Stock Trading Bot utilizing Deep Reinforcement Learning, specifically Deep Q-learning. It's designed for learning and experimentation, keeping the implementation simple and close to the algorithm discussed in research papers. The bot allows users to create intelligent agents that learn from market data, making decisions to buy, sell, or hold based on observed states. It incorporates several improvements to the Q-learning algorithm, including Vanilla DQN, DQN with fixed target distribution, Double DQN, Prioritized Experience Replay, and Dueling Network Architectures. Users can train the agent on historical data and evaluate its performance, with visualizations available for model evaluations. It's a valuable resource for those interested in applying reinforcement learning to financial trading.

TrafficFlowPrediction is an open-source project designed for predicting traffic flow using various neural network architectures, including Stacked Autoencoders (SAEs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). This tool is ideal for researchers and data scientists working in transportation planning and traffic management. It requires Python 3.6, Tensorflow-gpu 1.5.0, Keras 2.1.3, and scikit-learn 0.19. Users can train models with their own data, with experiment data from the Caltrans Performance Measurement System (PeMS) provided as an example. The project offers detailed metrics like MAE, MSE, RMSE, MAPE, R2, and Explained variance score for each model, demonstrating its effectiveness in traffic forecasting.

DeepCTR-Torch is a comprehensive, open-source Python package designed for building and experimenting with deep learning-based Click-Through Rate (CTR) models, leveraging the PyTorch framework. It offers a modular and extensible architecture, allowing users to easily implement and customize a wide range of CTR models, including popular architectures like DeepFM, xDeepFM, and Wide & Deep. The package includes numerous core component layers, enabling data scientists and researchers to construct their own custom models efficiently. With its user-friendly API, DeepCTR-Torch simplifies the process of training and predicting with complex models using standard `model.fit()` and `model.predict()` functions, making it an invaluable tool for recommendation systems and advertising applications.

DeepFakeDefenders is an open-source image forgery recognition algorithm designed to detect deepfakes and manipulated images. This tool is valuable for security applications and for ensuring the authenticity of digital content. It supports training from scratch using pretrained models like RepLKNet and ConvNeXt, and offers both multi-GPU and single-GPU training options. Users can assemble models and perform inference to obtain deepfake scores. The project also provides Docker support for easy deployment, making it accessible for developers and researchers to integrate into their workflows for robust image verification.

deep-learning-uncertainty is an open-source repository dedicated to predictive uncertainty estimation in deep learning models. It offers a comprehensive literature survey, detailed paper reviews, and experimental setups for various baseline methods. The repository also includes a collection of implementations, making it a valuable resource for researchers and engineers. This tool is designed to help users understand, quantify, and improve the reliability of predictions made by deep learning models, addressing critical aspects of model trustworthiness and robustness. It serves as a central hub for exploring established and emerging techniques in uncertainty quantification.

MOA (Massive Online Analysis) is a popular open-source framework designed for Big Data stream mining. It provides a comprehensive suite of machine learning algorithms, including classification, regression, clustering, outlier detection, concept drift detection, and recommender systems. Built in Java, MOA is related to the WEKA project but is specifically engineered to handle more demanding, large-scale, and real-time data stream processing challenges. The framework is extensible, allowing users to integrate new mining algorithms, stream generators, or evaluation measures, and serves as a benchmark suite for the stream mining community.

Eyeballer is a convolutional neural network designed by Bishop Fox for analyzing penetration testing screenshots. It helps security professionals identify "interesting" targets from a vast collection of web-based hosts, particularly useful in large-scope network penetration tests. Users can employ their favorite screenshotting tools like EyeWitness or GoWitness, then process the outputs through Eyeballer to categorize them. The tool labels screenshots into categories such as "Old-Looking Sites" (indicating potential vulnerabilities), "Login Pages" (suggesting further functionality and credential enumeration opportunities), "Webapp" (signifying a larger attack surface), "Custom 404's" (to filter out uninteresting pages), and "Parked Domains" (to remove invalid attack surfaces from scope). Eyeballer provides results in both human-readable HTML and machine-readable CSV formats, offering performance metrics like Overall Binary Accuracy and All-or-Nothing Accuracy.

Ogoodo offers custom-built business management systems tailored to a company's specific workflows and terminology. Unlike off-the-shelf solutions, Ogoodo provides a dedicated manager who analyzes business processes, develops the system, and handles ongoing management and updates, eliminating the need for in-house IT expertise. Key features include inventory management, quotation issuance, customer lists, automated document delivery via KakaoTalk, SMS, and email, and robust security with automatic backups. The system is designed for ease of use, requiring no installation or extensive training, and ensures data integrity and security. It supports unlimited data and members, with customizable access controls and automatic updates, making it suitable for businesses seeking a highly personalized and managed operational solution.

Folionomics is a privacy-first crypto portfolio tracker designed to help users manage their digital assets across more than 70 blockchains, including EVM, Bitcoin, and Solana. It allows users to bundle multiple wallets into a single portfolio, providing a consolidated view of balances, allocations, and DeFi positions. The platform supports NFT tracking and enables direct token swaps from the dashboard by querying multiple aggregators for the best routes. Folionomics emphasizes security by being read-only, never requiring private keys, and implementing safeguards like transaction target verification and price impact blocking. It also provides market data, candlestick charts, and analytics for CoinGecko-listed tokens.

Photonix is a modern, web-based photo management server designed to be run on a home server. It enables users to efficiently find specific photos from their collection on any device, leveraging advanced machine learning algorithms for smart filtering. Key AI capabilities include object recognition, face recognition, location awareness, and color analysis. While currently in development and not yet feature-complete for version 1.0, it offers a robust foundation for organizing and searching large photo libraries. The project encourages community contributions and can be easily set up using Docker Compose, making it accessible for technical users to deploy and test its features.

Notreload AI is an intelligent financial news tracking system designed to help users stay ahead of Wall Street. It leverages AI technology to monitor thousands of financial sources around the clock, providing instant alerts on breaking news, significant price changes, and emerging market trends. This ensures users never miss critical opportunities. The platform offers a dynamic feed of market events, including earnings movers, news-driven stocks, analyst upgrades/downgrades, and event-driven stocks. Users can sign up to receive breaking news alerts the moment they happen, keeping them informed and enabling faster decision-making in the fast-paced financial markets.

squeezeDet is an open-source project providing a TensorFlow implementation of SqueezeDet, a convolutional neural network specifically designed for real-time object detection. This tool is particularly optimized for autonomous driving applications, emphasizing a unified, small, and low-power architecture. It allows users to train and evaluate object detection models using datasets like KITTI, supporting various network backbones such as SqueezeNet, ResNet50, and VGG16. The repository includes scripts for installation, demo execution, training, and validation, making it a comprehensive resource for researchers and developers working on efficient object detection in resource-constrained environments.

Stock-trading-ml is an open-source stock trading bot designed to leverage machine learning for making stock price predictions. This tool allows users to train their own models, edit model architectures, and customize dataset preprocessing. It supports Python 3.5+ and relies on libraries such as alpha_vantage, pandas, numpy, sklearn, keras, tensorflow, and matplotlib. Users can save stock price history to CSV files, train models using either basic or technical indicator approaches, and then apply a trading algorithm based on the newly saved model. The project is available on GitHub under the GPL-3.0 license, making it accessible for developers and data scientists interested in algorithmic trading.

CW Aero Services Pte Ltd is a leading provider of comprehensive solutions for the aviation industry, focusing on aircraft on the ground. The company offers a range of services including Airport Systems, Ground Support Equipment (GSE), and Maintenance Tooling. They also specialize in Engineering and Test Systems, providing component test benches, industrial machinery, and custom engineering design. Furthermore, CW Aero delivers advanced Digital Solutions such as Airport Fleet Management, LIDAR Aircraft Towing Collision Avoidance, Predictive Maintenance, Industrial IoT, and asset tracking. Their offerings are designed to enhance efficiency, safety, and sustainability for airlines, MROs, and airports across Southeast Asia.

whylogs is an open-source data logging library designed to provide visibility into data quality and machine learning model performance over time. It allows users to generate summaries of datasets, called whylogs profiles, which capture key statistical properties like distributions, missing values, and custom metrics. These profiles are efficient, customizable, and mergeable, enabling logging for distributed and streaming systems. whylogs facilitates the detection of data drift, training-serving skew, and model performance degradation. It also supports data quality validation in model inputs or data pipelines, exploratory data analysis of massive datasets, and data auditing and governance across organizations. The library integrates with various data and ML pipeline tools and offers a profile visualizer for interactive reports.

PresalesIQ is an AI-native presales management platform designed for sales engineering and solutions consulting teams. It automates engagement tracking, activity capture, and operational analytics, eliminating the need for manual data entry and CRM workarounds. The platform provides real-time visibility into deal risk, team utilization, and forecast accuracy, allowing leaders to intervene earlier and make data-driven decisions. PresalesIQ structures workflows instantly using AI, ensuring that engagement context and risk signals are captured the moment work happens. This helps sales engineers and solutions consultants focus on customers rather than data entry, leading to faster execution and improved technical win rates. It integrates with existing CRM platforms, calendar systems, and Jira for product gap routing.

AIMEDIC Operator is a B2B AI layer designed for healthcare institutions in Colombia, integrating seamlessly with existing HIS and other data sources like ERPs and analytical warehouses. It automates critical administrative tasks such as generating RIPS (Registro Individual de Prestación de Servicios de Salud), reducing glosas (claim denials) through pre-billing validation, and ensuring regulatory compliance with Colombian health laws like Ley 1581. The platform allows users to query data and generate dashboards using natural language, eliminating the need for SQL or specialized technical knowledge. It focuses on enhancing operational efficiency, providing real-time insights, and adapting to evolving regulatory standards without requiring a replacement of current core systems.

ModelFox simplifies the entire machine learning lifecycle, from training to deployment and monitoring. Users can train models directly from CSV files using a command-line interface, with automatic data transformation and model selection. It supports predictions across multiple programming languages including Elixir, Go, JavaScript, PHP, Python, Ruby, and Rust, providing flexibility for integration into diverse applications. The platform also offers a browser-based application for inspecting models, tuning performance, making example predictions with detailed explanations, and monitoring models in production to track accuracy, precision, and recall, as well as detect data drift.

SurroundOcc is an advanced AI tool developed for multi-camera 3D occupancy prediction, primarily targeting autonomous driving applications. It reconstructs comprehensive and consistent 3D scenes by extracting multi-scale features from camera images and lifting them to 3D volume space using spatial cross-attention. The tool then applies 3D convolutions for progressive upsampling and multi-level supervision. A key differentiator is its pipeline for generating dense occupancy ground truth from sparse LiDAR points, leveraging existing 3D detection and semantic segmentation labels without requiring extra human annotations. This process fuses multi-frame LiDAR points for dynamic objects and static scenes separately, followed by Poisson Reconstruction and voxelization to create dense volumetric occupancy. SurroundOcc supports both occupancy prediction and ground truth generation on custom data, offering flexibility for researchers and developers in the autonomous driving domain.

smartcore is a comprehensive, fast, and ergonomic open-source library designed for machine learning and numerical computing in Rust. It enables developers to apply machine learning algorithms leveraging first principles, covering a broad range of methods including linear models, tree-based methods, ensembles, SVMs, neighbors, clustering, decomposition, and preprocessing. The library emphasizes production-friendly APIs, strong typing, and good defaults, while remaining flexible for research and experimentation. It features strong linear algebra traits with optional ndarray integration, WASM-first defaults for portability, and practical utilities for model selection, evaluation, and data access. smartcore is ideal for developers building AI applications in Rust who need robust and efficient ML capabilities.

Cturtle is a comprehensive platform that utilizes big data and AI to address critical talent shortages by tracking and engaging global talent. It forms strategic partnerships with corporations, governments, and universities to connect international alumni and skilled migrants with suitable opportunities worldwide. The platform offers various solutions like TalentConnect for live talent engagement, Incomes Outcomes for tracking international alumni careers, and UniAdvisor for university rankings based on international student incomes. Additionally, Cturtle provides JOB+ AI Employer Introductions for graduates and iGlobal Talent for AI employer introductions for skilled migration, making it a powerful tool for global talent acquisition and retention.