{"id":15159349,"url":"https://github.com/debasishray16/stockpredictor","last_synced_at":"2025-10-24T16:30:54.295Z","repository":{"id":225633222,"uuid":"766207752","full_name":"debasishray16/StockPredictor","owner":"debasishray16","description":"This project basically aims to provide a visual representation and comparative analysis of close price data related to different company ticker. 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It works on LSTM architecture which is an advanced version of RNN (Type of Neural Navigated Network).\n\n- In this project, we have incorporated different data-preprocessing techniques for continuous dataset value.\n- Also, in this project, we have incorporated XG-Boost as ensemble method to increase accuracy of following LSTM model.\n\n**NOTE: All test and research work are done and are pushed in different repository related to this project trained on different epoch cycles and parameters, which are usable with project.**\n\n--\u003e **\u003ca href=\"https://github.com/debasishray16/Stock-Prediction-Models\"\u003e Link to Model Training Repository\u003c/a\u003e**\n\n## Description\n\n- Our project works on concepts of deep learning to predict values based on time-series model. It includes use of LSTM (Long Short Term Memory) with XG-Boost to enhance the performance of prediction. Also, it takes tickers as input and gives prediction results.\n\n- This repository invloves deployment of model with **two-interfaces**.\nOne with \u003ckbd\u003e Streamlit-App \u003c/kbd\u003e  [**[Link](https://ticker-prediction-app-tpa.streamlit.app/)**] and \u003ckbd\u003e React App \u003c/kbd\u003e\n\n- For versioning of project deployement,We have created docker images for respective project to track and observe major changes made in the application.\nEach of these images are tagged with different versions which can be easily pulled into your system.\n\n## Installation\n\n### 1. Running locally after cloning\n\nTo run this project as website on your local system.Follow the steps -\n\n- Navigate to \u003ckbd\u003eTicker Predictor Website\u003c/kbd\u003e folder.\n\n```bash\ncd \"Ticker Predictor Website\"\n```\n\n- Now, navigate to \u003ckbd\u003e Front-end\u003c/kbd\u003e folder.\n\n```bash\ncd \"Front-end\"\n```\n\n- Run command in  if project is cloned for first time. *This will install necessary node_modules folder in current folder.*\n\n```bash\nnpm install\n```\n\n- Now to start the development server in React. Run the command:\n\n```bash\nnpm run start\n```\n\n- Simultaneously, Run command in **another terminal** to start **backend server** created on python.\n- Navigate to \u003ckbd\u003e Backend\u003c/kbd\u003e folder and run bash command.\n\n```bash\npython waitress_server.py\n```\n\n![terminal_Screenshot](assets/images/terminal_screenshot.png)\n\n```text\nNote: This will start the server and will connect with React website directly, running on https://localhost/3000.\n```\n\nIt should look like this after starting the server.\n\n![website_Preview](assets/images/Website_Preview.png)\n\n### 2. Running project using Docker\n\n1. To run website located in \u003ckbd\u003e Ticker Predictor Website\u003c/kbd\u003e. Follow these steps:\n\n- Open docker desktop and start the docker engine.\n- Open **one terminal** for backend and open **another terminal** for frontend image to run and execute container.\n\n```bash\n# Terminal 1\n# this will pull and run latest deployed frontend container.\ndocker run -p 3000:3000 debasishray/predictor-frontend:latest\n```\n\n```bash\n# Terminal 2\n# this will pull and run latest deployed backend container\ndocker run -p 5000:5000 debasishray/predictor-backend:latest\n```\n\n- Now, navigate to any web-browser and type:\n\n```text\nhttps://localhost/3000\n```\n\n2. To run streamlit-webapp located in \u003ckbd\u003e Ticker Streamlit App\u003c/kbd\u003e deployed on Docker. Follow these steps:\n\n- Open docker desktop and start the docker engine.\n- Open terminal and then, type the command:\n\n```bash\n# this will run latest deployed streamlit-app container\ndocker run -p 8501:8501 debasishray/streamlit-app:latest\n```\n\n- Then, click on link provided on docker terminal.\n\n## Steps to deploy on Github Packages\n\n1. Create a replica of Docker image with different tag.\n2. Check the image created.\n3. Authenticate by using **PAT (Personal Access Token)**.\n4. Push that image in GitHub Packages.\n\n```bash\ndocker tag debasishray/streamlit-app:v1.0 webapp\n\ndocker tag webapp ghcr.io/debasishray16/stockpredictor/webapp:latest\ndocker image ls\n\n# For authentication\necho \"pat-value\" | docker login ghcr.io -u debasishray16 --password-stdin\n\n# ghcr.io/\u003cusername\u003e/\u003crepository\u003e\ndocker push ghcr.io/debasishray16/stockpredictor/webapp:latest\n```\n\n### Contributors\n\n\u003cdiv align=\"center\"\u003e\n \u003ca href=\"https://github.com/debasishray16/StockPredictor/graphs/contributors\"\u003e\n   \u003cimg src=\"https://contrib.rocks/image?repo=debasishray16/StockPredictor\" /\u003e\n \u003c/a\u003e\n\u003c/div\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdebasishray16%2Fstockpredictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdebasishray16%2Fstockpredictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdebasishray16%2Fstockpredictor/lists"}