StockWise

Predicting Stock Movement using Random Forest and LSTM

The proposed model has the potential to revolutionize the field of stock price prediction by combining the strengths of Random Forest and LSTM. The accurate and consistent prediction of stock prices will enable investors to make informed decisions about their investments. Overall, the "Predicting Stock Movement using Random Forest and LSTM" project was successful in creating a software system that forecasts stock prices utilizing two potent machine learning algorithms: Random Forest and Long Short-Term Memory (LSTM). The major objective of the project was to create a tool that would allow traders, investors, and financial experts to predict stock prices with accuracy. Data preprocessing, feature extraction, model selection, and model training were some of the stages that went into developing the system. The predictive models were constructed using the Random Forest and LSTM algorithms. Accuracy, precision, MSE, and RMSE were some of the measures that were used to assess the system's performance. The outcomes demonstrated that the system performed with a high degree of accuracy, precision, and recall. Users of the system can receive trustworthy stock price predictions, which can aid in making wise investment choices and more effectively managing financial risk. Overall, this study effectively illustrated how machine learning algorithms like Random Forest and LSTM can be used to reliably predict stock prices. The creation and assessment of the system can act as a starting point for further study and the creation of more complex and sophisticated predictive models.

Live Demonstration link

Project: Stock Price Prediction with Machine Learning

This repository contains a Python project that combines financial data retrieval, machine learning, and GUI development to predict stock prices using various methods. The application fetches stock data using the yfinance library, preprocesses the data, and trains predictive models, including a Random Forest classifier and an LSTM neural network, to forecast future stock prices.

Features:

• Data retrieval from Yahoo Finance using yfinance.
• GUI interface built with PyQt5 for user interaction.
• Implementation of a Random Forest classifier for stock price direction prediction.
• Development of an LSTM neural network model for stock price prediction.
• Visualization of actual vs. predicted stock prices using matplotlib.

Technologies Used:

• Python
• PyQt5
• yfinance
• pandas
• numpy
• sklearn
• keras (for LSTM model)
• matplotlib

Getting Started:

1. Clone the repository:

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

2. Install the required Python packages:

   pip install PyQt5 yfinance pandas numpy scikit-learn keras matplotlib

3. Run the application:

   python main.py

Usage:

1. Enter a valid Yahoo Finance stock code in the GUI.
2. Click the "Predict" button to initiate stock price prediction.
3. View the Random Forest predictions and LSTM predictions in the output area.

Disclaimer:

Remember, this application's predictions are based on historical data and machine learning models. They should not be considered as absolute guarantees of future performance. It's crucial to conduct your own research and consider various factors before making any investment decisions.

#Sample Images

1. The PyQt5 user interface:

   

2. The prediction for tommorow:

   

3. The Visual Representation:

   

##Acknowledgments:

• This project was inspired by the need to demonstrate stock price prediction techniques.
• Thanks to the authors of the libraries and frameworks used in this project.