churn_model_new

XGBoost Churn Prediction Model with JFrog ML

Overview

This project employs the XGBoost algorithm for customer churn prediction. It's implemented using the JFrog ML and the XGBoost library.

Features

• Custom XGBoostChurnPredictionModel Class Definition: Customizes the base QwakModel to work with the XGBoost algorithm for churn prediction.

• Model Initialization: Initializes the XGBoost model with user-defined or default hyperparameters. The model is trained on a customer churn dataset and fine-tuned for optimal performance.

• Churn Prediction via JFrogm ML's Predict API: Utilizes JFrog ML's Predict API for assessing the probability of customer churn based on various features like account length, area code, international plan, etc.

Functionality

The primary functionality is to predict the probability of customer churn. The code is designed for seamless integration with JFrog ML and serves as a practical example for churn prediction tasks.

How to Test Locally

1. Clone the Repository: Clone this GitHub repository to your local machine.

2. Install Dependencies: Make sure you have the required dependencies installed, as specified in the conda.yml file.

   conda config --set ssl_verify false
   conda env create -f main/conda.yml
   conda activate churn_model

3. Install and Configure the JFrog ML SDK: Use your account JFrog ML API Key to set up your SDK locally.

   pip install qwak-sdk
   qwak configure
   pip install "qwak-inference[batch,feedback]"

4. Run the Model Locally: Execute the following command to test the model locally:

   python test_model_locally.py

How to Run Remotely on JFrog ML

1. Build on JFrog ML:

   Create a new model on JFrog ML using the command:

   qwak models create "Churn Prediction Model" --project "Sample Project"

   Initiate a model build with:

   qwak models build --model-id <your-model-id> ./churn_model_new

2. Deploy the Model on JFrog ML with a Real-Time Endpoint:

   To deploy your model via the CLI, use the following command:

   qwak models deploy realtime --model-id <your-model-id> --build-id <your-build-id>

3. Test the Live Model with a Sample Request:

   Install the Qwak Inference SDK:

   pip install qwak-inference

   Call the Real-Time endpoint using your Model ID from the Qwak platform:

   python test_live_mode.py <your-qwak-model-id>

Project Structure

.
├── main                   # Main directory containing core code
│   ├── __init__.py        # An empty file that indicates this directory is a Python package
│   ├── model.py           # Defines the Churn Model
│   ├── data.csv           # Defines the data to train the Model
│   └── conda.yaml         # Conda environment configurationdata
|
├── test_model_locally.py  # Script to test the model locally
├── test_live_model.py     # Script to test the live model with a sample REST request
└── README.md              # Documentation