Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Stock Return Prediction Using Knn Svm Guassian Process Adaboost Tree Regression And Qda | 85 | 2 years ago | mit | Jupyter Notebook | ||||||
Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks | ||||||||||
Datapipeline | 57 | 7 years ago | Python | |||||||
Real time stock data pipeline --play with Kafka, Cassandra, Spark, Redis, Node.js, Zookeeper | ||||||||||
Stock Prediction | 24 | 6 years ago | 2 | gpl-3.0 | Python | |||||
Machine learning pipeline for training TensorFlow models to forecast stock prices. Written in Python. | ||||||||||
Hk_ipo_prediction | 20 | 7 years ago | Jupyter Notebook | |||||||
Predict first day performance of Hong Kong IPO stocks: A pipeline example of machine learning projects | ||||||||||
Real Time Stock Analyzer | 16 | 6 years ago | Shell | |||||||
Bigdata Pipeline | ||||||||||
Hashtagcashtag | 13 | 9 years ago | Scala | |||||||
insight data engineering project | ||||||||||
Machinelearning_smartbeta_pipeline | 6 | 8 years ago | Python | |||||||
ML pipeline for SmartBeta momentum factor on equity portfolio | ||||||||||
Real Time Stock Streaming Pipeline | 6 | 6 years ago | Python | |||||||
Stock Prediction Portfolio Optimization | 5 | 4 years ago | Jupyter Notebook | |||||||
A Streamlit based application to predict future Stock Price and pipeline to let anyone train their own multiple Machine Learning models on multiple stocks to generate Buy/Sell signals. This is a WIP and I will keep on adding new ideas to this in future. | ||||||||||
Drugmarket | 5 | 4 years ago | bsd-2-clause | Python | ||||||
Collect and analyze data related to clinical trials of publicly listed biotech and pharmaceutical stocks. This is intended to give a clinical pipeline and financial overview of many securities. |