Customer Churn Prediction Machine Learning Project

(Replace with your project image)

Table of Contents

1. Introduction
2. Project Overview
3. Dataset
4. Installation
5. Usage
6. Model Training
7. Model Evaluation
8. Deployment
9. Contributing
10. License
11. Contact

Introduction

Welcome to our Customer Churn Prediction Machine Learning Project! This project aims to predict customer churn in a telecommunications company using machine learning techniques. Customer churn refers to the phenomenon where customers discontinue using a company's products or services. By identifying potential churners, the company can take proactive measures to retain them, ultimately reducing customer attrition and improving overall business performance.

This readme provides an overview of the project, including instructions for installation, usage, model training, evaluation, and deployment.

Project Overview

The project is structured around the following key steps:

1. Data Collection: Acquiring customer data, including relevant features such as customer demographics, usage behavior, and historical interactions.

2. Data Preprocessing: Cleaning, transforming, and preparing the data for model training. This step involves handling missing values, encoding categorical variables, and scaling numerical features.

3. Model Selection: Choosing appropriate machine learning algorithms for churn prediction. We'll explore various models and select the one that best suits our requirements.

4. Model Training: Training the selected model on the preprocessed data using a suitable training strategy.

5. Model Evaluation: Assessing the performance of the trained model using evaluation metrics such as accuracy, precision, recall, and F1-score.

6. Deployment: Integrating the trained model into a deployable system, enabling real-time churn prediction.

Dataset

The dataset used for this project contains historical information about customers, including features like customer ID, age, gender, account type, usage patterns, service subscription details, and whether the customer churned or not. The dataset is split into training and testing sets for model development and evaluation, respectively.

Note: The dataset used for this project is proprietary and not publicly available.

Installation

To run this project locally, follow these steps:

1. Clone this repository to your local machine.

git clone https://github.com/your-username/your-repository.git
cd your-repository

2. Create a virtual environment (optional but recommended).

python -m venv venv
source venv/bin/activate   # On Windows, use: venv\Scripts\activate

3. Install the required dependencies.

pip install -r requirements.txt

Usage

To use the churn prediction model, follow the instructions in the churn_prediction.ipynb notebook provided in the repository. This notebook guides you through the entire process, from data preprocessing to model evaluation.

Model Training

The model training process involves the following steps:

1. Data loading and preprocessing
2. Feature selection and engineering
3. Model selection
4. Model training and hyperparameter tuning

Refer to the churn_prediction.ipynb notebook for detailed implementation.

Model Evaluation

We evaluate the model's performance using various metrics such as accuracy, precision, recall, and F1-score. The evaluation results are presented in the notebook as well.

Deployment

For deploying the churn prediction model into a production environment, we recommend using a web service, API, or integrating it into the company's existing systems. Unfortunately, we cannot provide the deployment instructions in this repository, as it depends on the company's infrastructure and requirements.

Contributing

We welcome contributions to this project! If you find any issues or want to improve the code, feel free to open a pull request.

License

This project is licensed under the MIT License.

Contact

If you have any questions or suggestions regarding the project, please feel free to contact us at your-email@example.com. We appreciate your interest and feedback!

---