Artificial Intelligence and Machine Learning Repo-1 🤖

Welcome to our AI and Machine Learning Repository!

🚀 Explore the World of AI/ML! This repository is a treasure trove of fascinating AI projects that blend innovation, creativity, and cutting-edge techniques—dive in and be inspired!

We're thrilled to have you here! 🌟

🙌 Maintainers 👩‍💻 :

• Anusha Arora

🙌 Collaborators :

• Arni
• Charushi
• Devanshi

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📂 Project Details

1️⃣ VibeLift

👩‍💼 Description:
VibeLift is a polished, tablet-inspired web application designed to help users track and improve their emotional well-being. By utilizing Natural Language Processing (NLP), the app analyzes user-submitted text to predict their current emotion and provides tailored wellness exercises to "lift" their vibe.

👾 Project Category: Machine Learning

🌟 Details:

• Provides tailored wellness exercises
• NLP

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2️⃣ Moodify

🏏 Description:
A web application built with Streamlit and scikit-learn that classifies the dominant emotion (e.g., joy, sadness, anger) from song lyrics and suggests related songs from a database with the same predicted 'vibe'.

👾 Project Category: Machine Learning

🌟 Details:

• Classifies the dominant emotion
• Provides related songs

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3️⃣ NINA (News Intelligence Neural Analyzer)

📚 Description:
NINA is a smart news verification tool that classifies articles as real or fake using state-of-the-art NLP embeddings and machine learning. By analyzing the content of news articles, it helps users identify potentially misleading information.

👾 Project Category: Machine Learning

🌟 Details:

• Identifies potentially misleading information
• Uses state-of-the-art NLP embeddings
• NLP

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4️⃣ ReMixRecipe

🥹 Description:
ReMixRecipe is a tool that helps you figure out what cuisine you can cook using the ingredients you already have. Instead of searching for recipes by name, you enter your leftover ingredients, and the system predicts which type of cuisine (like Italian, Mexican, or Indian) you can make. It uses a machine learning model trained on recipes to do this automatically. 👾 Project Category: Machine Learning

🌟 Details:

• System predicts which type of cuisine

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5️⃣ ResuMatch

💳 Description:
"ResuMatch is a simple, beginner-friendly web application designed to help job seekers optimize their resumes. It compares a resume PDF with a job description and provides a match percentage, along with highlighting missing keywords. This helps users improve their resume’s compatibility with Applicant Tracking Systems (ATS)."

👾 Project Category: Machine Learning

🌟 Details:

• Compares a resume PDF with a job description
• provides a match percentage

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6️⃣ CineSync

🏢 Description:
CineSync is a simple movie recommendation tool that helps users find movies similar to their favorites. Instead of complex algorithms, it compares plot summaries and genre tags to suggest movies with a similar story. Users type in a movie they like, and CineSync shows a list of “hidden gems” they might enjoy. It’s easy to use, built in Python, and can be run as a Streamlit web app.

👾 Project Category: Python

🌟 Details:

• Streamlit web app
• Helps users find movies similar to their favorites
• Compares plot summaries and genre tags

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7️⃣ Compare AI

🔗 Description:
This project detects similarity between two text documents to identify potential plagiarism. It uses Natural Language Processing (NLP) techniques to convert text into numerical representations and calculates similarity scores using machine learning methods.

👾 Project Category: Machine learning

🌟 Details:

• Natural Language Processing (NLP)
• Converts text into numerical representations

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8️⃣ Mythos AI

🧮 Description:
A machine learning project that predicts the main genre of a book using only its title.

👾 Project Category: Machine Learning

🌟 Details:

• User Friendly

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9️⃣ Seqnet

🎙️ Description:
Seqnet is a deep learning–based text generation project that predicts the next word in a sequence using an LSTM neural network built with TensorFlow and Keras.

👾 Project Category: Machine Learning

🌟 Details:

• TensorFlow and Keras
• LSTM

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🛠️ How to Get Started

1. Fork this Repository
   Click the Fork button to create your copy of this repository.

2. Clone the Repository

   git clone https://github.com/GDG-IGDTUW/AI-ML-1.git  
   cd repo-name  

3. Install Dependencies
   Navigate to the project folder you're interested in.
   For example:

   cd Sentiment-Analysis

   Load the dataset and Install necessary Libraries

4. Make Your Contributions

   • Perform EDA.
   • Train models.
   • Enhance Accuracy.
   • Add features.
   • Test your changes.

5. Submit a Pull Request
   Push your changes and create a pull request to propose your contributions! 🎉

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🤝 Contributing Guidelines

We ❤️ contributions! Follow these simple steps to contribute:

1. Browse through Issues and Choose any
   Browse the Issues tab and comment on the one you'd like to work on.

2. Clone the Repo, Make changes and Branch Out
   Create a new branch for your changes:

   git checkout -b feature-name  

3. Commit Your Work
   Write clear and concise commit messages:

   git commit -m "Add: Feature description"  

4. Push and PR
   Push your branch and create a pull request for review.

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🌟 Tips for Contributors

• Follow the repository’s code style and structure.
• Keep ML model training scripts well-indented and include comments.
• Share any interesting results or insights in the pull request description.
• If you want an issue to be assigned to you, Tag us and mention so under the issue.
• Please be patient and Feel free to Tag the maintainer or collaborators for any queries. ❤️

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Happy Coding and Collaborating!🚀❤️