rahul188 / ml-playbooks

🚖 RideShare Prediction Model

This project demonstrates a supervised learning workflow using a rideshare dataset. The script performs data loading, preprocessing, model training, and evaluation. Additionally, it integrates with New Relic for performance monitoring.

📑 Table of Contents

• 📥 Installation
• 🚀 Usage
• ⚙️ Configuration
• 📦 Dependencies
• 📜 License

📥 Installation

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Create a virtual environment:

   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. Install additional dependencies for Parquet support:

   pip install pyarrow  # or fastparquet

🚀 Usage

1. Configure New Relic:

   • Ensure you have a newrelic.ini file with the appropriate configuration, including the license_key.

2. Run the script:

   python supervised_learning.py

⚙️ Configuration

• New Relic Configuration:
  • The script initializes the New Relic agent using the newrelic.ini file. Ensure this file is present in the project directory and contains the necessary configuration.

📦 Dependencies

• pandas
• scikit-learn
• newrelic
• ml_performance_monitoring
• pyarrow or fastparquet (for Parquet file support)

Install all dependencies using:

pip install -r requirements.txt

📜 License

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

ml-playbooks's Issues

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