Stock Price Prediction Model
Stock Price prediction using news data. The datasets used consists news and stock price data from 2008 to 2016. The polarity(Subjectivity, Objectivity, Positive, Negative, Neutral) data is gathered from the news data and further used to predict stock prices. Achieved an accuracy of 94% using XGBoost.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Stock Prediction Models | 6,233 | a year ago | 46 | apache-2.0 | Jupyter Notebook | |||||
| Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations | ||||||||||
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| Stock Rnn | 1,339 | 2 years ago | 20 | Python | ||||||
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| Stockpriceprediction | 1,132 | 9 months ago | 10 | mit | Jupyter Notebook | |||||
| Stock Price Prediction using Machine Learning Techniques | ||||||||||
| Personae | 1,034 | 5 years ago | 8 | mit | Python | |||||
| 📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading. | ||||||||||
| Gekko Strategies | 829 | 4 years ago | 15 | JavaScript | ||||||
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| Trading Bot | 758 | 5 months ago | 16 | mit | Jupyter Notebook | |||||
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| Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall | ||||||||||
| Stocks_rnn | 405 | 8 years ago | 5 | apache-2.0 | Python | |||||
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Stock Price Prediction