Sample exercise using prophet and streamlit for stock predic
A research tool about Indian stock market
使用 Prophet 來預測股價, 如台積電輸入 2330.tw
du bao chung khoan
Forecast stock prices using RNN, LSTM, and GRU models
Predicts closing prices of various indices using LSTM
股票潛力股搜尋機(LSTM) 支援 美股 台股
Optimal Selling Strategy for Farmers
Predict stock prices using ARIMA and LSTM models
Predict future sales using CSV data and date range
Herramienta que ayudara a seguir la evolución de los precios
an app used to track trading details
Real-time stock trend prediction using news sentiment analys
Stockprediction is a web-based application designed to predict stock market trends using historical data. Built with Prophet and Streamlit, it provides a user-friendly interface for forecasting stock prices and analyzing market behavior. The tool leverages machine learning algorithms to offer accurate and actionable insights, helping users make informed investment decisions.
• Historical Data Analysis: Analyzes past stock performance to identify patterns and trends.
• Future Price Prediction: Uses machine learning to forecast future stock prices based on historical data.
• Interactive Visualizations: Displays detailed graphs and charts to help users understand market movements.
• Customizable Timeframes: Allows users to select specific date ranges for analysis.
• Accuracy Metrics: Provides performance metrics to evaluate the reliability of predictions.
• Real-Time Updates: Offers up-to-date stock data for accurate predictions.
streamlit run
to launch the web app.What data sources does Stockprediction use?
Stockprediction utilizes publicly available historical stock data from reputable financial databases.
How accurate are the predictions?
The accuracy depends on the quality of historical data and market conditions. While the model is trained to minimize error, it’s not 100% accurate due to market unpredictability.
Can I add new stocks to predict?
Yes. You can extend the app by adding more stock tickers to the dataset. Contact support for assistance.