{"id":25452820,"url":"https://github.com/yllvar/stock-prediction-ltsm","last_synced_at":"2025-05-16T13:10:34.659Z","repository":{"id":230098521,"uuid":"778476622","full_name":"yllvar/Stock-Prediction-LTSM","owner":"yllvar","description":"This project utilizes LSTM (Long Short-Term Memory) neural networks to predict stock prices for four different symbols: Apple (AAPL), Nvidia (NVDA), Google (GOOGL), and Amazon (AMZN). 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The LSTM models are trained using historical stock price data fetched from Yahoo Finance and evaluated based on various performance metrics.\n\n## Overview\n\nThe project consists of Python code written in Jupyter Notebook format (`stock_prediction_ltsm.ipynb`). It performs the following tasks:\n\n- Fetches historical stock price data from Yahoo Finance.\n- Prepares the data by selecting adjusted close prices, normalizing the data, and splitting it into training and testing sets.\n- Creates input-output pairs for training the LSTM models with a specified look-back period.\n- Defines the architecture of LSTM models, trains them, and evaluates their performance using metrics such as RMSE, MAE, MAPE, and R-squared.\n- Visualizes the results by plotting actual and predicted stock prices for the training data.\n\n## Installation and Setup\n\n### Clone Repository\n```bash\ngit clone https://github.com/yllvar/Stock-Prediction-LTSM.git\ncd Stock-Prediction-LTSM\n```\n\n### Python Installation\nEnsure you have Python installed. You can download it from [here](https://www.python.org/downloads/).\n\n### Install Dependencies\n```bash\npip install -r requirements.txt\n```\n\n### Jupyter Notebook Installation\nIf you want to execute the code in a Jupyter Notebook:\n```bash\npip install jupyterlab\n```\n\n---\n\n## Running the Code\n\n### Jupyter Notebook\n1. Open a terminal.\n2. Navigate to the project directory.\n3. Launch Jupyter Notebook.\n```bash\njupyter notebook\n```\n4. Open and run the `stock_prediction_ltsm.ipynb` notebook.\n\nThis code is inspired by Kuriko I. [Stock Price Prediction Using Deep Learning LSTM Network](https://www.linkedin.com/pulse/stock-price-prediction-using-deep-learning-lstm-network-kuriko-iwai-gdzgc/?utm_source=share\u0026utm_medium=guest_mobile_web\u0026utm_campaign=copy\u0026fbclid=IwAR3BKny2Ji0VJoMjUsbLY-LSHHFsGTImdI7Oggy3jmKN3-F-abLGDDgqHW4_aem_ASQgZIM0tiWHGazOS55-sRHzVgGQklphOD6Gsa517zv0LdKQjn2tbqSqrtm6q2nriBBVPdLJD-g4MFOCMNh7MNm8)\n---\n\n## Acknowledgements\n- This project utilizes various libraries such as NumPy, pandas, Matplotlib, TensorFlow, and scikit-learn for data manipulation, visualization, and machine learning tasks.\n- Data fetching is performed using the yfinance library.\n- The code structure and methodologies are inspired by machine learning literature and online resources.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyllvar%2Fstock-prediction-ltsm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyllvar%2Fstock-prediction-ltsm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyllvar%2Fstock-prediction-ltsm/lists"}