Machine Learning Course Repository 🎓

Welcome to the Machine Learning Course repository! This comprehensive collection showcases various machine learning projects, from predictive modeling to data visualization and analysis. Each project is carefully documented and structured to provide hands-on experience with different aspects of machine learning.

📁 Repository Structure

ml_course/
├── projects/
│   ├── bulldozer_price_prediction/
│   │   ├── end-to-end-bulldozer-price-regression.ipynb
│   │   └── note.md
│   │
│   ├── heart_disease/
│   │   ├── end-to-end-heard-disease-classification.ipynb
│   │   ├── test.png
│   │   └── note.md
│   │
│   ├── matplotlib_examples/
│   │   └── note.md
│   │
│   ├── numpy_examples/
│   │   └── note.md
│   │
│   ├── pandas_examples/
│   │   └── note.md
│   │
│   └── scikit_learn/
│       └── note.md
│
├── data/                     # Centralized data storage for all projects
├── env/                     # Environment configurations
├── etc/                    # Additional configurations
├── environment.yml         # Main environment specification
├── .gitignore             # Git ignore rules
└── README.md              # This documentation

🚀 Projects Overview

1. Bulldozer Price Prediction

• Type: Regression Analysis
• Purpose: Predict auction sale prices of bulldozers
• Key Features:
  • Time series data handling
  • Advanced feature engineering
  • Model evaluation and optimization

2. Heart Disease Classification

• Type: Binary Classification
• Purpose: Predict heart disease presence
• Key Features:
  • Medical data analysis
  • Feature importance analysis
  • Model comparison

3. Matplotlib Examples

• Type: Data Visualization
• Purpose: Master plotting techniques
• Features:
  • Basic and advanced plots
  • Customization techniques
  • Interactive visualizations

4. NumPy Examples

• Type: Numerical Computing
• Purpose: Array operations and mathematics
• Features:
  • Array manipulations
  • Mathematical operations
  • Performance optimization

5. Pandas Examples

• Type: Data Analysis
• Purpose: Data manipulation techniques
• Features:
  • Data cleaning
  • DataFrame operations
  • Data transformation

6. Scikit-learn Examples

• Type: Machine Learning
• Purpose: ML algorithm implementation
• Features:
  • Model selection
  • Hyperparameter tuning
  • Cross-validation

💾 Data Organization

The data/ directory contains all datasets used across projects:

• Structured by project type
• Raw and processed data
• Consistent naming conventions
• Version controlled (where appropriate)

🛠️ Getting Started

1. Clone the Repository:

   git clone <repository-url>
   cd ml_course

2. Environment Setup:

   conda env create -f environment.yml
   conda activate ml-course

3. Project Navigation:

   • Each project has its own note.md with specific instructions
   • Follow the Jupyter notebooks for step-by-step implementation

📚 Documentation

• Project Notes: Each project contains a detailed note.md
• Notebooks: Step-by-step implementation guides
• Code Comments: Inline documentation
• Data Dictionary: Available in the data directory

🔧 Dependencies

• Python 3.x
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Scikit-learn

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Submit a pull request with detailed description

📝 License

This project is licensed under the MIT License.