Stock Market Price Prediction Web Application

View the live website

Click the link to view the website https://stock-price-predictor-using-ml.onrender.com/

Project Overview

This project demonstrates an end-to-end web application developed in Python for predicting stock market prices. Leveraging machine learning techniques, the application provides predictions for future stock prices based on historical data. The project integrates data collection, model training, and web interface functionalities, offering a complete solution for stock market price forecasting.

Features

• Data Collection: Gathers historical stock market data using APIs or web scraping techniques.
• Data Preprocessing: Cleans and prepares data for model training, including handling missing values and feature scaling.
• Model Training: Utilizes machine learning algorithms to train a predictive model. Common algorithms include Linear Regression, LSTM, or other time-series models.
• Prediction: Generates stock price forecasts based on the trained model.
• Web Interface: Built with a Python web framework (e.g., Flask, Django, or Streamlit) to allow users to interact with the model and view predictions in real-time.
• Visualization: Displays historical data, predictions, and model performance metrics using charts and graphs.

Technologies Used

• Python: Core programming language for data analysis and web application development.
• Pandas & NumPy: Data manipulation and numerical computations.
• Scikit-Learn: Machine learning algorithms and tools.
• TensorFlow/Keras: For advanced models such as LSTM.
• Matplotlib/Seaborn: Data visualization.
• Flask/Django/Streamlit: Web framework for building the user interface.
• APIs/yfinance: For fetching real-time stock data.

Getting Started

1. Clone the Repository:

   git clone https://github.com/your-username/stock-market-price-prediction.git
   cd stock-market-price-prediction
   

2. Install Dependencies:

   pip install -r requirements.txt
   

3. Setup and Configuration:

• Configure any necessary API keys or environment variables.
• Adjust any settings for data sources and model parameters.

4. Run the Web Application:

   streamlit run app.py
   

5. Usage:

• Navigate to http://localhost:5000 (or the specified port) to access the web application.
• Input stock ticker symbols and view predictions and visualizations.

Website View

Contribution

Feel free to contribute to this project by submitting issues, feature requests, or pull requests. Please adhere to the project's coding standards and include tests for any new features.

License

This project is licensed under the APACHE 2.0 License. See the LICENSE file for more details