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🤖 Machine Learning & Data Science Repository ...

Python License Contributions Welcome

📚 Overview

Welcome to our comprehensive Machine Learning and Data Science repository! This project serves as a central hub for ML algorithms, data analysis tools, and practical implementations of various DS concepts.

🎯 Key Features

  • 📊 Data Preprocessing Tools
  • 🧮 Machine Learning Algorithms Implementation
  • 📈 Statistical Analysis Functions
  • 🔍 Exploratory Data Analysis Templates
  • 🤖 Deep Learning Models
  • 📱 Production-Ready ML Pipelines

🛠️ Technologies Used

  • Python - Core programming language
  • Scikit-learn - Machine learning library
  • TensorFlow/PyTorch - Deep learning frameworks
  • Pandas - Data manipulation and analysis
  • NumPy - Numerical computing
  • Matplotlib/Seaborn - Data visualization
  • Jupyter Notebooks - Interactive development

🚀 Getting Started

  1. Clone the repository
git clone https://github.com/yourusername/ML_DS.git
cd ML_DS
  1. Set up virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies
pip install -r requirements.txt

📖 Documentation

Detailed documentation for each module is available in the Wiki. This includes:

  • 📝 API Reference
  • 🎓 Tutorials
  • 💡 Best Practices
  • 🔍 Use Cases

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details on:

  • 📝 Code Style
  • 🔄 Pull Request Process
  • 🐛 Bug Reports
  • 💡 Feature Requests

📊 Project Status

  • ✅ Core ML Algorithms
  • ✅ Data Preprocessing Tools
  • 🚧 Deep Learning Models (In Progress)
  • 📅 API Development (Planned)

📫 Contact & Support

📄 License

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

🙏 Acknowledgments

  • Special thanks to all contributors
  • Inspired by [relevant projects/people]
  • Built with support from [organizations/communities]

⭐ Star this repository if you find it helpful!

Directory structure: └── ajaykr2712-ml_ds/ ├── README.md ├── Evaluation/ │ ├── Eval.md │ └── metrics.md ├── Learning Logistic regression/ │ ├── Logistic_Reggr.md │ ├── Logistic_reuglar.py │ ├── Regularized_Logistic_Regression_Implementation.py │ ├── b.txt │ ├── logistic_regression_from_scratch.py │ ├── visual.md │ ├── visual.py │ └── ML_DS/ ├── ML_Scratch/ │ ├── ml.md │ └── ml.py ├── Projects/ │ ├── 02.End-to-End-ML-Project.ipynb │ ├── Work_Implementation.ipynb │ ├── concep2.md │ ├── concep3.md │ ├── concep4.md │ ├── concept10.md │ ├── concept11.md │ ├── concept5.md │ ├── concept6.md │ ├── concept7.md │ ├── concept8.md │ └── concept9.md ├── Strange/ │ ├── Expert.ipynb │ ├── tweet_emotions.csv │ ├── .DS_Store │ └── Books/ │ └── Artificial_Intelligence_A_Modern_Approach.md ├── certs/ │ └── Top_ML_certs.md └── .ipynb_checkpoints/ └── Work_Implementation-checkpoint.ipynb