Customer Churn Prediction Project

The Customer Churn Prediction project is designed to predict customer churn using a machine learning model deployed via a Flask API. This project helps businesses identify customers who are likely to leave, allowing for proactive retention strategies.

Table of Contents

• Project Overview
• Getting Started
• API Endpoints
• Request Format
• Response Format
• Error Handling

Project Overv

Objective

The primary objective of this project is to predict customer churn based on historical customer data. The project consists of two main components: a machine learning model and a Flask API for model deployment.

Components

1. Machine Learning Model: This component involves the training and evaluation of a machine learning model using historical customer data. The model is designed to predict whether a customer is likely to churn or not.

2. Flask API: The Flask API serves as an interface to the machine learning model. It allows users to send customer data and receive churn predictions in real-time.

Getting Started

Running the API

To run the Flask API, execute the following command:

python FlaskPredictiveService.py

By default, the API runs on http://localhost:5000.

API Endpoints

Predict Endpoint URL: /predict Method: POST Description: Make predictions using the pre-trained machine learning model.

Request Format

Input Data The input data should be provided in JSON format with the following fields:

CustomerID: The unique identifier for the customer. Name: The customer's name. Age: The age of the customer

Response Format

The API response will be in JSON format and will include churn predictions.

Example Response:

{ "predictions": [0, 1, 0, 0, 1] }

Error Handling

In case of any errors, the API will return an error message in JSON format.

Example Error Response:

{ "error": "Invalid input data." }