Popular GitHub Repositories for Historical Newspapers

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.

The Internet plays an increasingly important part in our daily lives as a source of written content for news and leisure. Yet it is tedious and difficult to sort through this staggering flow of information and stay updated with changes in our world, even using automated tools. Reading magazines and newspapers is too time-consuming, and there is a huge amount of online content that is updated or generated each minute. Our solution considers each user’s interests and leverages Artificial Intelligence, Machine Learning and Natural Language Processing in order to suggest to relevant articles from the internet.

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.

Tutorial on NE processing for Digital Humanities - DH Utrech 2019

Stock Market Decision Support System using Deep Learning & Sentiment Analysis

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

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.

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.

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

Objective is to develop a predictive model for a consumer finance company to identify potential loan defaulters. By analyzing historical loan data, & diff. data the factors that influences loan default rate.

Platform providing market news & forecasting tools for forex traders. Uses LSTM model to predict exchange rates using historical OHLC data & sentiment analysis.