Skip to content

Latest commit

 

History

History
72 lines (51 loc) · 2.24 KB

README.md

File metadata and controls

72 lines (51 loc) · 2.24 KB

🛍️ Retail Sales Prediction Web Application

Developed during the GDG Hackathon 2024 at RIT, Rajapalayam, this project integrates web technologies and predictive analytics for retail sales based on age and gender.

💻 Technology Stack

Frontend

  • HTML: Structuring web content 🖥️
  • DaisyUI: UI component library of TailwindCSS 🌟
  • JavaScript: Adding interactivity and dynamic elements ⚡

Backend

  • Flask (Python): Lightweight web framework for building the server-side logic 🐍
  • Machine Learning Libraries:
    • scikit-learn: Implementing the Random Forest Classifier 🌳
    • pandas: Data manipulation and analysis 🧑‍💻
    • numpy: Numerical computations 🔢

📸 Screenshots

🏠 Front Page

FrontPage

🔮 Predictor Page

PredictorPage

📈 Result Page

ResultPage

🔧 How to Use

  1. Clone the Repository:

    git clone https://github.com/atheeq-rhxn/GDG-Hackathon-2024
cd GDG-Hackathon-2024

  2. Install Dependencies:

    pip install -r requirements.txt

  3. Run the Flask Server:

    flask run

  4. Open a browser: Visit http://127.0.0.1:5000.

Acknowledgments

A huge thank you to GDG RIT for organizing the hackathon and supporting this project.

GDG TEAM:

  • Kavya V - Organizer - LinkedIn
  • Harini P - Technical Core Lead
  • Dheetchana K - Cloud Lead
  • Hariharasubramani M - Cyber Security and Ethical Hacking - LinkedIn
  • Sri Saru Kumar S - UI/UX Lead
  • Hari Krishnan A - AI/ML Lead - LinkedIn
  • HARIHARA SUTHAN G - Android Lead - LinkedIn
  • Baalekshan E - Executive Lead