https://github.com/derrickbaruga7/pytorch-machine-learning
A repository showcasing the creation and testing of production-ready machine learning models, emphasizing data processing optimization, pipeline efficiency, and rigorous testing for deployment.
https://github.com/derrickbaruga7/pytorch-machine-learning
data-science machine-learning marketing predictive-modeling pytorch
Last synced: 3 months ago
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A repository showcasing the creation and testing of production-ready machine learning models, emphasizing data processing optimization, pipeline efficiency, and rigorous testing for deployment.
- Host: GitHub
- URL: https://github.com/derrickbaruga7/pytorch-machine-learning
- Owner: derrickbaruga7
- Created: 2024-07-24T18:57:28.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-01-07T20:59:28.000Z (6 months ago)
- Last Synced: 2025-02-08T10:20:27.829Z (5 months ago)
- Topics: data-science, machine-learning, marketing, predictive-modeling, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 332 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Machine Learning Model Development and Testing
This repository contains the code and workflow for creating, testing, and deploying machine learning models. The focus is on optimizing model accuracy, improving data processing pipelines, and ensuring robust deployment of production-ready systems.
## Features
• Model Development: Creating machine learning pipelines using advanced algorithms (e.g., LSTMs).
• Data Ingestion: Optimized data pipelines for faster processing of large datasets.
• Testing & Validation: Comprehensive quality assurance to ensure high model performance and pre-deployment reliability.
• Deployment: End-to-end deployment processes for scalable and efficient machine learning solutions.
## Technologies Used
• Programming: Python
• Machine Learning Libraries: Scikit-learn, TensorFlow, PyTorch
• Data Processing: Pandas, NumPy
• Deployment: Docker, Flask/FastAPI
• Version Control: Git
## How to Use
Clone this repository:
git clone https://github.com/your-username/ml-model-development.git
Navigate to the project directory:
cd ml-model-development
Install dependencies:
pip install -r requirements.txt
Run the Jupyter Notebook (model 2.ipynb) for training and testing the machine learning model.
## Future Improvements
• Expansion to support real-time predictions.
• Enhanced testing workflows with more advanced statistical validations.
• Integration with cloud services for automated ML.
A repository showcasing the creation and testing of production-ready machine learning models, emphasizing data processing optimization, pipeline efficiency, and rigorous testing for deployment.
Let me know if you’d like to customize further or add sections like examples of model outputs or diagrams!