Popular GitHub Repositories for Historical Odds

Using python and scikit-learn to make stock predictions

ARCHIVED: Contains historical course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL

? Open Source App Framework to build streaming apps with real-time data - ? Build real-time data pipelines and make real-time data universally accessible - ? Join historical and real-time data in the stream to create smarter ML and AI applications. - ? Standardize complex data ingestion and stream data to apps with pre-built connectors

Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

Stay ahead of AI trends with automated Reddit insights! ? This tool scans AI-related Reddit communities in both English and Chinese, using DeepSeek R1 to analyze posts and comments, summarize key discussions, and track historical trends. Daily rankings highlight the hottest topics, helping you catch emerging trends before they go mainstream.

Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch

This is a project of build knowledge graph course. The project leverages historical stock price, and integrates social media listening from customers to predict market Trend On Dow Jones Industrial Average (DJIA).

Stock Market Trend Prediction using sentiment analysis Leveraging machine learning and sentiment analysis, we accurately forecast stock market trends. Our project combines advanced algorithms like BERT and Na?ve Bayes with sentiment analysis from Twitter and other sources. By analyzing sentiment and historical price data, we provide insights

Calculate technical indicators from historical stock data Create features and targets out of the historical stock data. Prepare features for linear models, xgboost models, and neural network models. Use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. Evaluate performance of the models in order to optimize them Get predictions with enough accuracy to make a stock trading strategy profitable.

In these notebooks the entire research and implementation process carried out for the construction of various machine learning models based on neural networks that are capable of predicting levels of solar radiation is captured given a set of historical data taken by meteorological stations.

An advanced machine learning model utilizes a Random Forest Regressor to generate betting recommendations for Major League Baseball (MLB) games.

Tools to train and explore diachronic word embeddings from Big Historical Data

Tutorial on NE processing for Digital Humanities - DH Utrech 2019

Stock Market Decision Support System using Deep Learning & Sentiment Analysis

An NER tool for ancient place names based on Pleiades and Spacy.

This project implements a time series multivariate analysis using RNN/LSTM for stock price predictions. A deep RNN model was created and trained on five years of historical Google stock price data to forecast the stock performance over a two-month period.

A quantitative trading strategy backtester with an interactive dashboard. Enables users to implement, test, and visualise trading strategies using historical market data, featuring customisable parameters and key performance metrics. Developed with Python and Polars.

In this project RNN variations are tested on a dataset comprised of not only Bitcoin historical price, but also other influencing factors such as macroeconomic indices, sentiments etc.

In the banking industry, detecting credit card fraud using machine learning is not just a trend; it is a necessity for banks, as they need to put proactive monitoring and fraud prevention mechanisms in place. Machine learning helps these institutions reduce time-consuming manual reviews, costly chargebacks and fees, and denial of legitimate transactions. Suppose you are part of the analytics team working on a fraud detection model and its cost-benefit analysis. You need to develop a machine learning model to detect fraudulent transactions based on the historical transactional data of customers with a pool of merchants.

This repository is part of an NLP course for humanities and cultural studies. This course uses historical newspapers as a source and applies NLP methods to them. NLP tasks: Tokenization, Lemmatization, TF-IDF, Part-of-speech tagging, semantic search with transformers, article extraction and OCR post-correction with LLMs, NER and text classification