Stock Market Analysis

Features

• Stock Price Predictions: Uses models like Linear Regression, Random Forest, Gradient Boosting, XGBoost, Support Vector Regressor, Multi-Layer Perceptron, and LSTM to forecast stock prices.
• Technical Indicators: Analyzes popular indicators such as Moving Averages, RSI, MACD, etc.
• Interactive GUI: Visualizes stock performance and prediction results using Matplotlib and Plotly.
• Feature Engineering: Automated feature selection using SelectKBest for improved accuracy.
• Hyperparameter Tuning: Optimizes models with GridSearchCV.

Technology Stack

• Frontend: PyQt5
• Backend: Python, Machine Learning models (Scikit-learn, TensorFlow, XGBoost)
• Data Sources: yfinance for stock data
• Libraries Used:
  • pandas for data manipulation
  • matplotlib & plotly for visualization
  • seaborn for statistical plots
  • scikit-learn for machine learning algorithms and utilities
  • tensorflow & keras for deep learning (LSTM)
  • xgboost for gradient boosting algorithms

Installation

1. Clone the repository:

   git clone https://github.com/aseng21/Stock_Market_Analysis
   cd Stock_Market_Analysis

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   python app.py

Usage

1. Select a Stock: Enter a stock ticker (e.g., AAPL, TSLA) to retrieve historical data.
2. Choose Model & Indicator: Select a prediction model and any technical indicators you'd like to analyze.
3. Train & Predict: The app will train the selected model and display predictions along with relevant charts.
4. View Results: Analyze the predicted stock price, model performance metrics, and visual trends.

Models Supported

• Linear Regression
• Random Forest
• Gradient Boosting
• XGBoost
• Support Vector Regressor
• Multi-Layer Perceptron (MLP)
• Long Short-Term Memory (LSTM)

Contributing

Feel free to contribute by submitting a pull request. Please ensure all changes are tested and documented.

Future Enhancements

• Add more machine learning models and techniques.
• Improve the GUI with additional customization options.
• Implement advanced portfolio management tools.
• Add support for cryptocurrency predictions.