Data & Analytics

Augtech NextWealth IT Services Private Limited is an ISO 9001:2015 certified organization providing Information Technology and Information Technology Enabled Services. They focus on delivering world-class "Data Enrichment" and "Customer Interaction" services to clients in AI/ML tech, E-commerce, Fin-Tech, Education, and other sectors. Their expertise includes data collection from diverse sources, data preparation involving cleansing, consolidation, normalization, and validation, and data enrichment for AI/ML models, including multimedia annotation. The company also offers customer service operations, including inbound and outbound support. Augtech NextWealth is a social impact organization committed to providing opportunities to talent in Tier-2 and Tier-3 ecosystems.

Business Machine Learning (BML) and Business Data Science (BDS) Applications is a comprehensive, open-source resource available on GitHub, offering a curated list of practical applications across diverse business functions. This repository provides insights and examples for Accounting, Customer, Employee, Legal, Management, and Operations, making it a valuable reference for professionals and researchers. It details specific projects such as predictive modeling with GitHub logs, satellite data analysis for financial forecasting, and data imputation techniques. The resource also highlights opportunities for collaboration with Sov.ai, a company focused on integrating advanced machine learning with financial data analysis, and includes a wide range of research and project opportunities.

Contextify is a powerful Chrome extension designed to enhance web content interaction. Users can effortlessly select any text snippet on a webpage and apply custom-prompted AI actions. This tool allows for a wide range of functionalities, including translating text into different languages, summarizing lengthy articles, rewriting content for clarity or different tones, and brainstorming ideas based on selected text. Contextify tailors its responses to specific user prompts, making it highly versatile for various tasks. It aims to streamline workflows and provide immediate, context-aware AI assistance directly within the browser, revolutionizing how users engage with online information.

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.

DIPY (Diffusion Imaging in Python) is a comprehensive open-source Python library designed for the analysis of MR diffusion imaging and other 3D/4D+ medical images. It provides a robust set of generic methods for tasks such as spatial normalization, signal processing, machine learning, and statistical analysis. Beyond general medical image processing, DIPY specializes in computational anatomy, offering advanced techniques for diffusion, perfusion, and structural imaging. The library is intended for research purposes, with a clear disclaimer for clinical deployment. It supports installation via pip or conda and adheres to Scientific Python SPEC 0 for version compatibility, making it accessible for researchers and developers in the medical imaging field.

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.

Hylos is a distributor sales intelligence platform designed to automate sell-through data collection and deliver real-time regional insights. It eliminates the need for manufacturers to chase distributor reports by providing a secure, frictionless way for distributors to submit sales data. The platform unifies diverse data formats and SKU codes through autonomous data mapping, creating a single source of truth for sales reps, directors, and leadership. Hylos offers live territory health monitoring, automated sell-through tracking, and AI-driven growth signals, enabling manufacturers to spot regional surges and dips, flag opportunities, and manage inventory risks proactively. This allows businesses to move from manual, reactive reporting to strategic, data-driven decision-making, ensuring distributors have the right stock at the right time.

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.

2 Minute Warning is a specialized video analytics company designed to assist sports teams with film breakdown. Through its dedicated Hudl extension, coaches can seamlessly submit game footage for analysis directly from the Hudl platform. The service efficiently processes the submitted footage, providing comprehensive data, detailed reports, and insightful scout cards. This information is delivered within an hour, offering teams a significant competitive advantage by enabling rapid analysis of opponent strategies and team performance. The tool focuses on streamlining the film review process, allowing coaches to quickly identify strengths, weaknesses, and key player tendencies.

Layerbrain is a research and development lab dedicated to inventing new architectures for generally intelligent machines. Their core focus is on creating systems that can observe work processes, infer the current state of operations, autonomously operate software, and continuously learn from the outcomes of their actions. The lab aims to build systems that are increasingly self-sufficient, with the ultimate goal of having the lab itself run by the AI systems it creates. This approach targets the development of advanced AI agents capable of understanding and interacting with complex environments.

linusrants offers a unique dataset comprising Linus Torvalds' rants from the kernel mailing list, spanning from 2012 to 2015. Each rant has been classified by its negativity using sentiment analysis, providing a 'hate' score. The dataset is available in various formats including table, JSON, pickle, and TSV, with the TSV format offering extensive metadata for in-depth analysis. This resource is ideal for researchers, data scientists, and developers interested in sentiment analysis, linguistic patterns, or the communication style of prominent figures in the open-source community. Users can also build the classification system themselves using the provided Python script.

ShopiTrack is a comprehensive analytics platform specifically designed for Shopify stores, offering deep insights into customer behavior and revenue attribution. It provides real-time analytics, session replay, and heatmaps to help merchants understand why visitors convert or drop off. The tool traces every session back to its source, allowing users to identify which marketing channels, campaigns, and referrers drive actual revenue. Key features include live visitor journeys, detailed traffic intelligence, and the ability to rewind and replay individual visitor sessions to see every tap, scroll, and decision. ShopiTrack aims to help Shopify owners fix drop-offs and significantly increase conversion rates by providing actionable data.

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.

LSTM-Sentiment-Analysis is a GitHub repository offering resources for performing sentiment analysis with Long Short-Term Memory (LSTM) networks in Tensorflow. It provides an iPython notebook, training data, and a pre-trained LSTM model, enabling users to analyze sentiment from custom text inputs. The repository is designed to accompany an O'Reilly tutorial, making it suitable for those learning about or implementing sentiment analysis. It includes instructions for setting up the environment with TensorFlow, NumPy, Jupyter, and matplotlib, as well as guidance for Docker users. This tool is ideal for developers and data scientists looking to implement or experiment with LSTM-based sentiment analysis.

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.

Stock.Indicators for .NET is a C# NuGet package designed to convert raw equity, commodity, forex, or cryptocurrency financial market price quotes into essential technical indicators and trading insights. This library is crucial for developers building investment tools for algorithmic trading, technical analysis, machine learning, or visual charting. It provides a comprehensive set of indicators like moving averages, Relative Strength Index, Stochastic Oscillator, and Parabolic SAR. Version 3 introduces robust streaming capabilities, allowing for real-time and incremental data processing with three calculation styles: Series (batch), BufferList (incremental), and StreamHub (real-time with observable patterns). This makes it highly adaptable for various data processing needs.

Channel Profiler is a YouTube analysis tool designed to help creators optimize their content strategy and grow their channels. It offers instant analytics with zero learning curve, allowing users to get forensic data on titles, tags, and outlier videos. Key features include a Niche Finder to discover fast-growing channels and trending niches, an AI Analyzer that decodes channel strategy and audience sentiment, and tools to pinpoint optimal publish times. Users can also reverse-engineer viral titles and extract top-performing SEO tags to improve visibility. The platform helps visualize trends and identify video durations that drive the most growth, making it ideal for creators who approach YouTube growth scientifically.

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.

spark-py-notebooks is a comprehensive collection of IPython/Jupyter notebooks designed to educate users on various Apache Spark concepts using Python (pySpark). The tutorials range from fundamental to advanced topics, focusing on Big Data Analysis and Machine Learning. Users can learn about RDD creation, basic RDD operations like map, filter, and collect, sampling, set operations, and data aggregations. The collection also delves into working with key/value pair RDDs and introduces MLlib for basic statistics, exploratory data analysis, logistic regression, and decision trees. Additionally, it covers Spark SQL for structured processing with DataFrames and includes applications like building a movie recommendation web service.

TradeMetricX is a professional trading journal and analytics platform designed for day traders, forex traders, and stock traders to enhance their performance. It provides advanced analytics to deep dive into trading performance, revealing strengths and weaknesses through detailed metrics, win rates, and profit factors. The platform features an intuitive P&L calendar for visualizing daily, weekly, and monthly performance, and a comprehensive trading journal to document thoughts, emotions, and market observations. Additionally, TradeMetricX includes a trade replay feature for candle-by-candle review, stock research with real-time data and TradingView charts, and aggregated market news to keep traders informed. It supports CSV imports from major brokers and offers a free plan for up to 50 trades per month.