steveuseful/model_training

Model Training is a comprehensive toolkit for developing, training, and validating machine learning models, specifically tailored for churn prediction, featuring robust data preprocessing, model optimization, and evaluation techniques to enhance predictive accuracy.

Model Training Project

Introduction

This project is dedicated to developing and training machine learning models for churn prediction. It aims to provide an accurate and efficient tool for identifying potential customer churn, helping businesses to proactively address this issue.

Features

• Churn Prediction: Utilizes historical data to predict customer churn.
• Advanced Machine Learning Techniques: Incorporates the latest algorithms and practices in machine learning.
• Performance Metrics: Includes detailed evaluation metrics to assess model accuracy and performance.

Getting Started

Prerequisites

Before you begin, ensure you have met the following requirements:

pip install numpy
pip install pandas
pip install scikit-learn

## Installation
Clone the repository and install the required packages:

bash
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git clone https://github.com/SteveUseful/model_training.git
cd model_training
pip install -r requirements.txt

## Usage
Here's a quick example of how to train and predict using the model:

python
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from model_training import Trainer

trainer = Trainer(data_path="path/to/your/data.csv")
model = trainer.train()
predictions = model.predict("path/to/your/newdata.csv")
Contributing
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

## Fork the Project
Create your Feature Branch (git checkout -b feature/AmazingFeature)
Commit your Changes (git commit -m 'Add some AmazingFeature')
Push to the Branch (git push origin feature/AmazingFeature)
Open a Pull Request

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

## Authors
Steve - Initial work - SteveUseful