Best AI tools for< Predict >
20 - AI tool Sites
AI Sports Betting Predictions
The AI Sports Betting Predictions website offers advanced AI-powered sports prediction services. Users can access accurate predictions for various sports events, enhancing their betting experience. The platform is currently undergoing updates and will resume predictions by the first week of September 24. New user registrations are closed, and interested individuals are encouraged to download the COPA football prediction app for continued access to predictions.
Setlist Predictor
Setlist Predictor is an AI tool designed to help music enthusiasts and concert-goers stay ahead by predicting the setlist of their favorite artists. By leveraging the latest data and AI technology, users can arrive prepared for their next gig with insights on what songs to expect. The tool offers a seamless experience, allowing users to search for any artist and receive a predicted setlist. Setlist Predictor aims to enhance the concert experience and ensure that users never miss a beat.
Predict API
The Predict API is a powerful tool that allows you to forecast your data with simplicity and accuracy. It uses the latest advancements in stochastic modeling and machine learning to provide you with reliable projections. The API is easy to use and can be integrated with any application. It is also highly scalable, so you can use it to forecast large datasets. With the Predict API, you can gain valuable insights into your data and make better decisions.
Football Predictions AI
Football Predictions AI is a website that provides users with accurate and reliable football match predictions. Users can access a variety of prediction types, including 1x2, BTTS, Over Under, and Correct Score predictions for matches across different leagues. The site also offers live scores, articles, and settings to customize the user experience. With a focus on user privacy and data protection, Football Predictions AI aims to enhance the football betting experience for enthusiasts and fans.
PredictOPs
PredictOPs is an advanced AIOps platform powered by Gen-AI technology, redefining Operations Management with cutting-edge solutions. The platform offers real-time monitoring, actionable insights, alert correlation, microservice management, anomaly detection, and infrastructure log behavior analysis. It leverages adaptive algorithms and early warning systems to provide proactive solutions for failure rate analysis and trend identification. PredictOPs is scalable, reliable, and integrates Gen-AI for cognitive insights beyond traditional AIOps capabilities.
PredictModel
PredictModel is an AI tool that specializes in creating custom Machine Learning models tailored to meet unique requirements. The platform offers a comprehensive three-step process, including generating synthetic data, training ML models, and deploying them to AWS. PredictModel helps businesses streamline processes, improve customer segmentation, enhance client interaction, and boost overall business performance. The tool maximizes accuracy through customized synthetic data generation and saves time and money by providing expert ML engineers. With a focus on automated lead prioritization, fraud detection, cost optimization, and planning, PredictModel aims to stay ahead of the curve in the ML industry.
The Predictive Index
The Predictive Index is a talent optimization platform that offers personalized HR software to help organizations hire, develop, and retain top talent. It provides validated hiring assessments, leadership development, team development, employee engagement, and people management tools. The platform leverages behavioral data insights to optimize team management and employee development, supports meeting management with collaborative workspaces and automated summaries, and empowers leaders to provide continuous feedback and recognition. The Predictive Index is designed to help managers become effective leaders by fostering discretionary effort, team morale, and engagement through behavioral insights and leadership best practices.
Predis.ai
Predis.ai is an AI-powered application that offers predictive analytics solutions for businesses. It leverages advanced machine learning algorithms to analyze data and provide valuable insights to help companies make informed decisions. With a user-friendly interface, Predis.ai simplifies the process of data analysis and forecasting, making it accessible to users with varying levels of technical expertise. The application is designed to assist organizations in optimizing their operations, improving efficiency, and identifying trends to stay ahead in a competitive market.
Tickeron
Tickeron is an AI trading platform that offers a variety of tools and features to enhance trading in the stock market. It provides AI predictions for stocks, ETFs, forex, and other assets, empowering users with accurate stock predictions and trend insights. The platform includes AI robots for virtual accounts, trend predictions, pattern search engines, and real-time patterns. Additionally, Tickeron offers tools for traders and investors, such as stock portfolio wizards, active portfolios, model portfolios, and 401(k) portfolios. Users can also explore the marketplace for trading and investing tools, join trader and investor clubs, and access educational resources to improve their trading skills.
AI Baby Generator
AI Baby Generator is an AI application that predicts and generates ultra-realistic baby photos of your future child based on your photos and features. It offers customized baby photos, personality descriptions, and various packages to meet your needs. The application uses advanced AI technology to provide hyper-realistic baby images with a high facial match rate. With a user-friendly interface, it allows you to upload photos of yourself and your partner or even a celebrity crush to see what your future baby would look like. The generated baby photos can be shared on social media platforms and websites, and you have full ownership over them. AI Baby Generator ensures data privacy and offers a refund policy for basic orders within a specific timeframe.
赤ちゃんAC
赤ちゃんAC is an AI application that predicts the face of a baby by using AI technology called StyleGAN. Users can upload two images of parents' faces, and the AI analyzes and generates a high-resolution image of a baby's face with features resembling the parents. The application is user-friendly and offers the service of predicting baby faces from infancy to adulthood in six stages. It ensures security by deleting all image data within 24 hours. 赤ちゃんAC prohibits certain uses, such as using the generated images for profile icons, creating misleading representations, or engaging in adult content.
Numerai
Numerai is a data science tournament platform where users can compete to build models that predict the stock market. The platform provides users with clean and regularized hedge fund quality data, and users can build models using Python or R scripts. Numerai also has a cryptocurrency, NMR, which users can stake on their models to earn rewards.
Neurons
Neurons is a platform that uses AI to predict consumer responses and behavior. It offers a variety of solutions for businesses, including marketing agencies, designers, and e-commerce companies. Neurons' AI-powered tools can help businesses optimize their marketing campaigns, improve their product design, and better understand their customers.
BforeAI
BforeAI is an AI-powered platform that specializes in fighting cyberthreats with intelligence. The platform offers predictive security solutions to prevent phishing, spoofing, impersonation, hijacking, ransomware, online fraud, and data exfiltration. BforeAI uses cutting-edge AI technology for behavioral analysis and predictive results, going beyond reactive blocklists to predict and prevent attacks before they occur. The platform caters to various industries such as financial, manufacturing, retail, and media & entertainment, providing tailored solutions to address unique security challenges.
Heatseeker
Heatseeker is an AI-powered market experimentation tool that helps businesses predict customer preferences, conduct feature tests, and generate value propositions. It enables users to answer critical growth questions about market, audience, and product features through AI-powered experiments. Heatseeker provides insights into market trends, competitor analysis, and helps in making data-driven decisions. The platform offers curated recommendations, competitive intelligence, and continuous testing for refining strategies. It automates ad campaign generation, data collection, and provides recommendations for launching new products. Heatseeker is designed to help businesses optimize their marketing efforts and improve their product offerings.
MonkeeMath
MonkeeMath is an AI tool designed to scrape comments from Reddit and Stocktwits that mention stock tickers. It utilizes ChatGPT to analyze the sentiment of these comments, determining whether they are bullish or bearish on the outlook of the ticker. The data collected is then used to create charts and tables displayed on the website. Users can create an account to view predictions and participate in a prediction mini-game to earn a spot on the MonkeeMath user leaderboard.
ClosedLoop
ClosedLoop is a healthcare data science platform that helps organizations improve outcomes and reduce costs by providing accurate, explainable, and actionable predictions of individual-level health risks. The platform offers predictive analytics for various healthcare sectors, data science automation, and a healthcare content library to accelerate time to value. ClosedLoop's AI/ML platform is designed exclusively for the data science needs of modern healthcare organizations, enabling proactive interventions, improved clinical outcomes, and innovative healthcare offerings.
Lavo Life Sciences
Lavo Life Sciences is an AI-accelerated crystal structure prediction application that helps in drug development by providing accurate predictions. The platform offers solutions to de-risk pipelines, optimize solid-state formulations, and avoid late-stage surprises using AI technology. Lavo Life Sciences combines the expertise of chemists and engineers in AI and computational chemistry to deliver innovative solutions for drug development teams. The application aims to reduce turnaround time for crystal form identification, minimize risks of unexpected crystal forms, optimize drug formulations, and discover novel polymorphs with improved properties.
AutoPredict
AutoPredict is an AI application that predicts how long a car will last by analyzing over 100 million data points. It offers accurate estimates of a car's life span, providing users with valuable insights. The application also provides an API for integration with other businesses and a blog sharing insights and statistics from the AI model development.
Pecan AI
Pecan AI is a predictive analytics software product designed for business and data analysts. It offers blazing-fast predictions, seamless integrations, and requires no machine learning experience. Pecan empowers teams to succeed with impactful AI models, automates data preparation, and features a Predictive Chat, Predictive Notebook, and guided or DIY predictive modeling tools. The platform helps users build trustworthy predictive models, optimize campaigns, and make data-driven decisions to drive business growth.
20 - Open Source AI Tools
next-token-prediction
Next-Token Prediction is a language model tool that allows users to create high-quality predictions for the next word, phrase, or pixel based on a body of text. It can be used as an alternative to well-known decoder-only models like GPT and Mistral. The tool provides options for simple usage with built-in data bootstrap or advanced customization by providing training data or creating it from .txt files. It aims to simplify methodologies, provide autocomplete, autocorrect, spell checking, search/lookup functionalities, and create pixel and audio transformers for various prediction formats.
Tiny-Predictive-Text
Tiny-Predictive-Text is a demonstration of predictive text without an LLM, using permy.link. It provides a detailed description of the tool, including its features, benefits, and how to use it. The tool is suitable for a variety of jobs, including content writers, editors, and researchers. It can be used to perform a variety of tasks, such as generating text, completing sentences, and correcting errors.
skpro
skpro is a library for supervised probabilistic prediction in python. It provides `scikit-learn`-like, `scikit-base` compatible interfaces to: * tabular **supervised regressors for probabilistic prediction** \- interval, quantile and distribution predictions * tabular **probabilistic time-to-event and survival prediction** \- instance-individual survival distributions * **metrics to evaluate probabilistic predictions** , e.g., pinball loss, empirical coverage, CRPS, survival losses * **reductions** to turn `scikit-learn` regressors into probabilistic `skpro` regressors, such as bootstrap or conformal * building **pipelines and composite models** , including tuning via probabilistic performance metrics * symbolic **probability distributions** with value domain of `pandas.DataFrame`-s and `pandas`-like interface
NBA-Machine-Learning-Sports-Betting
This tool is a machine learning AI used to predict the winners and under/overs of NBA games. It takes all team data from the 2007-08 season to the current season, matched with odds of those games, and uses a neural network to predict winning bets for today's games. The tool achieves ~69% accuracy on money lines and ~55% on under/overs. It outputs expected value for teams' money lines to provide better insight and the fraction of your bankroll to bet based on the Kelly Criterion. A popular, less risky approach is to bet 50% of the stake recommended by the Kelly Criterion.
skyrim
Skyrim is a weather forecasting tool that enables users to run large weather models using consumer-grade GPUs. It provides access to state-of-the-art foundational weather models through a well-maintained infrastructure. Users can forecast weather conditions, such as wind speed and direction, by running simulations on their own GPUs or using modal volume or cloud services like s3 buckets. Skyrim supports various large weather models like Graphcast, Pangu, Fourcastnet, and DLWP, with plans for future enhancements like ensemble prediction and model quantization.
AlphaFold3
AlphaFold3 is an implementation of the Alpha Fold 3 model in PyTorch for accurate structure prediction of biomolecular interactions. It includes modules for genetic diffusion and full model examples for forward pass computations. The tool allows users to generate random pair and single representations, operate on atomic coordinates, and perform structure predictions based on input tensors. The implementation also provides functionalities for training and evaluating the model.
FBP
FootBallPrediction (FBP) is a software project that utilizes big data and machine learning to predict the outcome of football matches based on odds from gambling companies. The software has achieved an accuracy rate of over 80% in predicting match results. The current version, 22.0, successfully predicted eight out of nine matches from major football leagues. The project has a community of over 60 members who benefit from the predicted results. The author is seeking collaboration to further enhance the project and welcomes interested individuals to join. AI-FBP is a subscription service that provides daily football game predictions.
Scientific-LLM-Survey
Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.
admet_ai
ADMET-AI is a platform for ADMET prediction using Chemprop-RDKit models trained on ADMET datasets from the Therapeutics Data Commons. It offers command line, Python API, and web server interfaces for making ADMET predictions on new molecules. The platform can be easily installed using pip and supports GPU acceleration. It also provides options for processing TDC data, plotting results, and hosting a web server. ADMET-AI is a machine learning platform for evaluating large-scale chemical libraries.
imodels
Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. _For interpretability in NLP, check out our new package:imodelsX _
matsciml
The Open MatSci ML Toolkit is a flexible framework for machine learning in materials science. It provides a unified interface to a variety of materials science datasets, as well as a set of tools for data preprocessing, model training, and evaluation. The toolkit is designed to be easy to use for both beginners and experienced researchers, and it can be used to train models for a wide range of tasks, including property prediction, materials discovery, and materials design.
ExplainableAI.jl
ExplainableAI.jl is a Julia package that implements interpretability methods for black-box classifiers, focusing on local explanations and attribution maps in input space. The package requires models to be differentiable with Zygote.jl. It is similar to Captum and Zennit for PyTorch and iNNvestigate for Keras models. Users can analyze and visualize explanations for model predictions, with support for different XAI methods and customization. The package aims to provide transparency and insights into model decision-making processes, making it a valuable tool for understanding and validating machine learning models.
earth2studio
Earth2Studio is a Python-based package designed to enable users to quickly get started with AI weather and climate models. It provides access to pre-trained models, diagnostic tools, data sources, IO utilities, perturbation methods, and sample workflows for building custom weather prediction workflows. The package aims to empower users to explore AI-driven meteorology through modular components and seamless integration with other Nvidia packages like Modulus.
edge2ai-workshop
The edge2ai-workshop repository provides a hands-on workshop for building an IoT Predictive Maintenance workflow. It includes lab exercises for setting up components like NiFi, Streams Processing, Data Visualization, and more on a single host. The repository also covers use cases such as credit card fraud detection. Users can follow detailed instructions, prerequisites, and connectivity guidelines to connect to their cluster and explore various services. Additionally, troubleshooting tips are provided for common issues like MiNiFi not sending messages or CEM not picking up new NARs.
LLM-Microscope
LLM-Microscope is a toolkit designed for quantifying and visualizing language model internals. It provides functions for calculating anisotropy, intrinsic dimension, and linearity score. The toolkit also includes a Logit Lens feature for analyzing model predictions and losses. Users can easily install the toolkit using pip and explore the functionalities through provided examples.
interpret
InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...
pytorch-grad-cam
This repository provides advanced AI explainability for PyTorch, offering state-of-the-art methods for Explainable AI in computer vision. It includes a comprehensive collection of Pixel Attribution methods for various tasks like Classification, Object Detection, Semantic Segmentation, and more. The package supports high performance with full batch image support and includes metrics for evaluating and tuning explanations. Users can visualize and interpret model predictions, making it suitable for both production and model development scenarios.
CoachAI-Projects
This repo contains official implementations of **Coach AI Badminton Project** from Advanced Database System Laboratory, National Yang Ming Chiao Tung University supervised by Prof. Wen-Chih Peng. The high-level concepts of each project are as follows: 1. Visualization Platform published at _Physical Education Journal 2020_ aims to construct a platform that can be used to illustrate the data from matches. 2. Shot Influence and Extension Work published at _ICDM-21_ and _ACM TIST 2022_ , respectively introduce a framework with a shot encoder, a pattern extractor, and a rally encoder to capture long short-term dependencies for evaluating players' performance of each shot. 3. Stroke Forecasting published at _AAAI-22_ proposes the first stroke forecasting task to predict the future strokes of both players based on the given strokes by ShuttleNet, a position-aware fusion of rally progress and player styles framework. 4. Strategic Environment published at _AAAI-23 Student Abstract_ designs a safe and reproducible badminton environment for turn-based sports, which simulates rallies with different angles of view and designs the states, actions, and training procedures. 5. Movement Forecasting published at _AAAI-23_ proposes the first movement forecasting task, which contains not only the goal of stroke forecasting but also the movement of players, by DyMF, a novel dynamic graphs and hierarchical fusion model based on the proposed player movements (PM) graphs. 6. CoachAI-Challenge-IJCAI2023 is a badminton challenge (CC4) hosted at _IJCAI-23_. Please find the website for more details. 7. ShuttleSet published at _KDD-23_ is the largest badminton singles dataset with stroke-level records. - An extension dataset ShuttleSet22 published at _IJCAI-24 Demo & IJCAI-23 IT4PSS Workshop_ is also released. 8. CoachAI Badminton Environment published at _AAAI-24 Student Abstract and Demo, DSAI4Sports @ KDD 2023_ is a reinforcement learning (RL) environment tailored for AI-driven sports analytics, offering: i) Realistic opponent simulation for RL training; ii) Visualizations for evaluation; and iii) Performance benchmarks for assessing agent capabilities.
imodelsX
imodelsX is a Scikit-learn friendly library that provides tools for explaining, predicting, and steering text models/data. It also includes a collection of utilities for getting started with text data. **Explainable modeling/steering** | Model | Reference | Output | Description | |---|---|---|---| | Tree-Prompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/tree_prompt) | Explanation + Steering | Generates a tree of prompts to steer an LLM (_Official_) | | iPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/iprompt) | Explanation + Steering | Generates a prompt that explains patterns in data (_Official_) | | AutoPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/autoprompt) | Explanation + Steering | Find a natural-language prompt using input-gradients (⌛ In progress)| | D3 | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/d3) | Explanation | Explain the difference between two distributions | | SASC | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/sasc) | Explanation | Explain a black-box text module using an LLM (_Official_) | | Aug-Linear | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_linear) | Linear model | Fit better linear model using an LLM to extract embeddings (_Official_) | | Aug-Tree | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_tree) | Decision tree | Fit better decision tree using an LLM to expand features (_Official_) | **General utilities** | Model | Reference | |---|---| | LLM wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/llm) | Easily call different LLMs | | | Dataset wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/data) | Download minimially processed huggingface datasets | | | Bag of Ngrams | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/bag_of_ngrams) | Learn a linear model of ngrams | | | Linear Finetune | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/linear_finetune) | Finetune a single linear layer on top of LLM embeddings | | **Related work** * [imodels package](https://github.com/microsoft/interpretml/tree/main/imodels) (JOSS 2021) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible). * [Adaptive wavelet distillation](https://arxiv.org/abs/2111.06185) (NeurIPS 2021) - distilling a neural network into a concise wavelet model * [Transformation importance](https://arxiv.org/abs/1912.04938) (ICLR 2020 workshop) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies) * [Hierarchical interpretations](https://arxiv.org/abs/1807.03343) (ICLR 2019) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy * [Interpretation regularization](https://arxiv.org/abs/2006.14340) (ICML 2020) - penalizes CD / ACD scores during training to make models generalize better * [PDR interpretability framework](https://www.pnas.org/doi/10.1073/pnas.1814225116) (PNAS 2019) - an overarching framewwork for guiding and framing interpretable machine learning
forust
Forust is a lightweight package for building gradient boosted decision tree ensembles. The algorithm code is written in Rust with a Python wrapper. It implements the same algorithm as XGBoost and provides nearly identical results. The package was developed to better understand XGBoost, as a fun project in Rust, and to experiment with adding new features to the algorithm in a simpler codebase. Forust allows training gradient boosted decision tree ensembles with multiple objective functions, predicting on datasets, inspecting model structures, calculating feature importance, and saving/loading trained boosters.
20 - OpenAI Gpts
The Lottery Pro AI: Number Predictor
AI expert in lottery predictions for Mega Millions, Powerball, Cash 3, Fantasy 5, and all other state lotteries. Provides latest draw results and analysis.
AI predicts oil prices
Global oil price prediction expert, offering analysis and insights.
College entrance exam prediction app
Our college entrance exam prediction app uses advanced algorithms and data analysis to provide accurate predictions for students preparing to take their college entrance exams.
Soccer In-Play Predictions & Alerts
Provides live football (soccer) betting suggestions based on game stats and history. 75% success.
股票预测分析专家 | A股 | 实时数据
一款基于深度神经网络预测给出中国A股股票买入建议的智能投资顾问 An intelligent investment advisor based on deep neural network for predicting buy recommendations for Chinese A-share stocks.