Welcome to the MR-Analyzer repository! This is where I showcase my journey in machine learning and data science GenAI through various projects. Below you'll find an overview of each project along with links to their respective LinkedIn posts for a detailed description.

1. MR-Analyzer: Movie Recommendations
2. Rockmine Prediction Project
3. Diabetes Prediction Project
4. WhatsApp Chat Analyzer
5. GenAI MCQGenerator
6. GenAI AsktoAske
7. Getting Started
8. Contributing
9. License

🚀 Excited to share my latest project: MR-Analyzer - Movie Recommendations! 🎬

A movie recommendation system leveraging a custom dataset created by merging multiple sources along with the TMDB database. This project uses advanced machine learning algorithms to deliver personalized movie suggestions.

• Custom dataset integration
• TMDB database utilization
• Advanced Cosine Similarity & TF-IDF Vectorizer algorithms

• Website: MR-Analyzer - Movie Recommendations
• GitHub: MR-Analyzer Repository
• LinkedIn Post: Read more on LinkedIn

🔍 Rockmine Prediction Project

This project explores the depths of data with Sonar technology to understand underwater landscapes. Using Logistic Regression, we predict outcomes based on a dataset of 208 data points and 60 features.

• Logistic Regression model
• Training Accuracy Score: 84.34%
• Testing Accuracy Score: 76.19%

• Website: Rockmine Prediction Project
• GitHub: MR-Analyzer Repository
• LinkedIn Post: Read more on LinkedIn

🌊 Diabetes Prediction Project

A machine learning project aimed at diagnosing and managing diabetes mellitus. Using an SVM Classifier, the model predicts outcomes based on a dataset of 769 data points and 9 features.

• SVM Classifier model
• Training Accuracy Score: 78.66%
• Testing Accuracy Score: 77.27%

• Website: Diabetes Prediction Project
• GitHub: MR-Analyzer Repository
• LinkedIn Post: Read more on LinkedIn

Hello everyone, Introducing MR-Analyzer: Analyze WhatsApp Chats! 📊💬

An easy-to-use tool for analyzing WhatsApp conversations. This project harnesses the power of various technologies to provide comprehensive insights into your chat data.

• Technologies used: HTML, CSS, Bootstrap, Django, SQLite, NumPy, Pandas, Matplotlib, Seaborn
• User-friendly interface for chat data upload and analysis

• Website: WhatsApp Chat Analyzer
• GitHub: MR-Analyzer Repository
• LinkedIn Post: Read more on LinkedIn

Genai based mcq generator using langchain

The MCQ generator uses Gemini LLM and LangChain to create multiple-choice questions automatically. This tool generates diverse and high-quality questions, making it valuable for education and assessments.

• Technologies used: HTML, CSS, Javascript, Bootstrap, Django, SQLite,Langchain, GenAi LLM, Numpy, Pandas

• Website: MCQGenerator
• GitHub: MR-Analyzer Repository
• LinkedIn Post: Read more on LinkedIn

Genai based Question Answering realted to document!

Experience My QA Bot, powered by Langchain Gemini AI. It utilizes the text-Hugging face embeddings and the Gemini Pro LLM model, coupled with Pinecone Vector DB for efficient embeddings storage.

• Technologies used: HTML, CSS, Javascript, Bootstrap, Django, SQLite,Langchain, Gemini LLM, Hugging Face,Pinecone

• Website: asktoaske
• GitHub: MR-Analyzer Repositor
• LinkedIn Post: Read more on LinkedIn

To explore the projects, clone the repository and follow the instructions in each project's directory.

Make sure you have Python and Django installed on your machine.

1. Clone the repository:

   git clone https://github.com/anurag6569201/mr-analyzer.git
   cd mr-analyzer

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Navigate to the project directory and run the server:

   cd project-directory
   python manage.py runserver

5. Open your browser and go to http://127.0.0.1:8000/ to see the project in action.

Feel free to fork this repository, make improvements, and submit pull requests. Contributions are always welcome!

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Make your changes and commit them (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature-branch)
5. Open a Pull Request

This project is licensed under the MIT License. See the LICENSE file for details.

Stay tuned as we continue to innovate and leverage the power of machine learning for impactful insights!

For more details and updates, follow me on LinkedIn: Your LinkedIn Profile