Title: Tesla Stock Price Analysis and Prediction Web App

Brief Description

This project provides a user-friendly web application built with Streamlit to analyze Tesla (TSLA) stock prices and make predictions using various machine learning models. The app offers the following features:

• Historical Stock Price Visualization: View Tesla's closing prices along with technical indicators (RSI, Stochastic Oscillator).
• Date Filtering: Customize the displayed date range using an interactive sidebar.
• Prediction Models: Explore predictions generated by models including:
  • LSTM (Long Short-Term Memory)
  • SVM (Support Vector Machine)
  • LightGBM

Data Source Stock data is retrieved from Yahoo Finance (yfinance)

How to Run

1. Install Dependencies:

   pip install requirements.txt

2. Download the Code: Clone or download this repository.

3. Run the App:

   streamlit run app.py  # Replace 'app.py' if your file has a different name

   The app will launch in your web browser, typically at http://localhost:8501

Code Structure

• app.py (or similar): Contains the main Streamlit application logic.
• Import Statements: Ensure the code starts with necessary imports.
• Functions: Each core functionality is organized into well-defined functions (e.g., visualize_stock_price_history, build_and_train_model, etc.)

Using the Web App

1. Sidebar Options: Modify the date range and choose which indicators to display in the stock price chart.
2. Analysis Sections: Select the type of analysis you wish to perform:
   • "Stock Price History" for pure visualization with indicators.
   • "All Models" to compare prediction results from different models
   • Specific model options (LSTM, SVM, LightGBM) to focus on a single model.

Potential Improvements

• Additional Indicators: Add more technical indicators depending on your analysis needs.
• Model Refinement: Experiment with different model architectures and hyperparameters.
• Error Handling: Implement error handling for cases of insufficient data.
• Deployment/Hosting: Explore cloud-based services to make the app accessible to a wider audience.

Disclaimer This code and application are intended for educational and exploratory purposes. They should not be considered a substitute for professional financial advice.