Telco Customer Churn Prediction

Project Description

This is the implementation of my project for the course machine-learning-zoomcamp from DataTalksClub. The goal of this project is to deploy a web service for a machine learning model for customer churn in a Telco company.

Problem Statement

Customer churn is a important topic for the bottom line of most companies providing Business-to-Customer (B2C) services.

Dataset

The dataset used for this project has been sourced from Kaggle (see link).

Project Details

This repository has the following files:

• The data folder contains the dataset used in this project.
• The src folder contains the source code for the project.
• The web-service folder contains the code for deploying the trained model as a web service with Docker and Flask.
• The images folder contains screenshots and other images used in this README.md file.
• The Dockerfile defines the Docker image for the project, specifying the environment and dependencies required to run the code.
• The requirements.txt lists all the Python dependencies required for the project.

Quick Start

To get started with this project, do the following in the terminal:

1. Clone the repository:

git clone https://github.com/victornemenike/telco-customer-churn-prediction.git

2. Navigate to the project directory:

cd telco-customer-churn-prediction/

To prepare the project and install all dependencies, run the following:

3. Set up the environment:

Ensure you have a Python environment set up. You can create a virtual environment using:

python3.11 -m venv venv

source venv/bin/activate  # On Windows use `venv\Scripts\activate`

For more details on how the project was implemented, please see the Implementation Details section below.

Cloud

Amazon Web Services was used as the cloud provider for the model deployment.

Implementation Details

**1. Exploratory Data Analysis

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