Stockprediction

What is Stockprediction ?

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.

Features

• 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.

How to use Stockprediction ?

1. Install Required Libraries: Ensure you have Prophet and Streamlit installed.
2. Clone or Download the Repository: Get the Stockprediction codebase from the repository.
3. Run the Application: Use streamlit run to launch the web app.
4. Select a Stock Ticker: Choose the stock you want to analyze from the dropdown menu.
5. Choose a Timeframe: Specify the date range for prediction.
6. View Predictions: The app will generate and display historical data and future price forecasts.
7. Download Results: Option to download the predictions for further analysis.

Frequently Asked Questions

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.