Stock Price Prediction System

Overview

This project implements a stock price prediction system using machine learning techniques, specifically LSTM neural networks. The system fetches historical stock data, processes it, trains a predictive model, and visualizes the predicted vs. actual stock prices.

Prerequisites

Before running the code, ensure you have the following installed:

• Python 3.12
• Virtual environment (recommended)
• Required Python libraries listed in requirements.txt

Setup Instructions

1. Clone the Repository:

git clone <repository_url>

2. Create and Activate a Virtual Environment:

python -m venv stock_prediction_env
source stock_prediction_env/bin/activate   # On Windows: stock_prediction_env\Scripts\activate

3. Install Required Libraries:

pip install -r requirements.txt

Running the Code

1. Run the Main Script:

python main.py

This script will:

• Load and preprocess stock data.
• Train the LSTM model.
• Make predictions on test data.
• Visualize the predicted and actual stock prices.

2. Customize Parameters:

• Modify data_loading.py, model_training.py, or other modules to change stock symbols, training periods, model architecture, etc.

Project Structure

• data_loading.py Handles data fetching and preprocessing.
• model_training.py: Builds and trains the LSTM model.
• prediction.py: Generates predictions using the trained model.
• visualization.py: Visualizes the prediction results.
• main.py: Orchestrates the full workflow.