Stock Price Prediction AI Project

This AI project utilizes Long Short-Term Memory (LSTM) models for time series forecasting to predict stock prices. It includes various components and files to facilitate the prediction process.

Project Components

ML Model

• File: ML_Model_for_ADAMS_Stock.py
• Description: This file contains the Python code for training and implementing the LSTM model for stock price prediction.

Saved Model

• File: ADAMS_1.4.h5
• Description: The trained LSTM model is saved in this file for later use in making predictions.

Dataset

• File: Adam Sugar Mills Limited.csv
• Description: The dataset used for training and testing the model, containing historical stock price data.

Data Scraper

• File: data_scraper.py
• Description: A Python script used to scrape and collect stock price data from stock exchange.

Database

• File: stock.db
• Description: This SQLite database stores historical stock price data, stores new data fetched by 'data_scrapper.py' and the predictions made by model for easy retrieval and analysis.

Get Predictions

• File: get_predictions.py
• Description: A Python script to obtain stock price predictions using the trained LSTM model.

APP

• File: pred_stocks_app.py
• Description: The main application file, responsible for providing a user interface to interact with the model and view predictions.

Usage

1. Ensure you have all the required files and dependencies installed.
2. Run main to fetch data from database and make predictions.
3. Use the pred_stocks_app.py to visualize predictions in GUI.