https://github.com/arya2004/ai-ml-hack-week

AI/ML Hack Week Submissions
https://github.com/arya2004/ai-ml-hack-week

ai machine-learning nlp streamlit

Last synced: 5 days ago
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AI/ML Hack Week Submissions

• Host: GitHub
• URL: https://github.com/arya2004/ai-ml-hack-week
• Owner: arya2004
• Created: 2024-02-11T06:46:11.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-06-18T13:33:24.000Z (5 months ago)
• Last Synced: 2024-06-18T16:15:46.107Z (5 months ago)
• Topics: ai, machine-learning, nlp, streamlit
• Language: Jupyter Notebook
• Homepage:
• Size: 31.2 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# MLH Global Hack Week AI-ML Edition February Submissions

Welcome to our repository containing submissions for the MLH Global Hack Week AI-ML Edition February! Below, you'll find details about the three projects developed during the hackathon:

## Projects:

### 1. Email Spam Detector

- **Description**: This project aims to detect spam emails using machine learning algorithms. It utilizes natural language processing techniques to analyze the content of emails and classify them as spam or non-spam.

- **Technologies**: Python, scikit-learn, natural language processing (NLP), Streamlit, Pickle

### 2. Object Detection

- **Description**: The object detection project focuses on identifying and localizing objects within images or video streams. It utilizes deep learning models such as YOLO (You Only Look Once) or SSD (Single Shot Multibox Detector) for accurate detection.

- **Technologies**: Python, YOLOv8, PyTorch

### 3. Movie Recommendation System

- **Description**: This project aims to provide personalized movie recommendations to users based on their preferences and viewing history. It employs collaborative filtering algorithms and content-based filtering to suggest movies that users are likely to enjoy.

- **Technologies**: Python, Pandas, NumPy, scikit-learn, Streamlit, Pickle

## How to Use:

Each project directory contains its own set of instructions on how to set up and run the respective application. Follow the instructions provided in each project's README.md file to get started.

## Contributions:

We welcome contributions from the community to improve these projects further. If you have any suggestions, bug fixes, or feature enhancements, feel free to submit a pull request or open an issue.

## License:

This repository and all contributions are licensed under the [MIT License](LICENSE). Feel free to use and modify the code as per the terms of the license.

## Contact:

If you have any questions or need assistance, you can reach out to me.

Thank you for visiting our repository and exploring our projects! We hope you find them useful and inspiring.