Stock_price_predication
I have implemented Recurrent Neural Network (RNN model) to predict the future stock prices and compare it with linear regression.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Stock Market Prediction Web App Using Machine Learning And Sentiment Analysis | 460 | 3 months ago | 21 | mit | Python | |||||
| 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 | ||||||||||
| Stock Analysis | 366 | a year ago | 2 | mit | Jupyter Notebook | |||||
| Regression, Scrapers, and Visualization | ||||||||||
| Strtsmrt | 57 | 7 years ago | 2 | Python | ||||||
| Stock price trend prediction with news sentiment analysis using deep learning | ||||||||||
| Stock_price_predication | 14 | 6 years ago | Python | |||||||
| I have implemented Recurrent Neural Network (RNN model) to predict the future stock prices and compare it with linear regression. | ||||||||||
| Python_meets_finance | 9 | 5 years ago | Jupyter Notebook | |||||||
| Financial Analysis in Python | ||||||||||
| Stock_price_predictor | 7 | 7 years ago | Jupyter Notebook | |||||||
| Stock Price Predictor with Deep Learning | ||||||||||
| Stockx | 6 | 3 years ago | gpl-3.0 | Python | ||||||
| a python GUI application that performs technical analysis and forecasting of stock prices using linear regression | ||||||||||
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Python
Machine Learning
Stock
Recurrent Neural Networks
Linear Regression
Lstm Neural Networks