Model Training Project

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Technologies Used
• Contributing
• Dev

Introduction

The Model Training project aims to provide a comprehensive framework for training machine learning models. The goal is to facilitate the training process, from data preprocessing to model evaluation, offering tools and utilities that streamline the workflow.

Features

• Data preprocessing tools
• Model building and training pipeline
• Hyperparameter tuning
• Model evaluation metrics
• Integration with popular libraries like NumPy, NetworkX, and Requests

Installation

To install the Model Training project, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/modelTraining.git

2. Navigate to the project directory:

   cd modelTraining

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

To use the Model Training project, follow these steps:

1. Preprocess your data using the provided tools.
2. Build and train your model using the training pipeline.
3. Tune hyperparameters as necessary.
4. Evaluate your model using the provided metrics.

Example:

from preprocessing import clean_data
from training import train_model
from evaluation import evaluate_model

# Preprocess the data
data = clean_data('path/to/data.csv')

# Train the model
model = train_model(data)

# Evaluate the model
metrics = evaluate_model(model, data)
print(metrics)

Technologies Used

• Python 3.12.7
• Jinja2
• PyYAML
• NetworkX
• NumPy
• Requests
• SymPy

Contributing

We welcome contributions! To contribute to this project, follow these steps:

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

Dev

[email protected]