Telco Customer Churn Prediction Project

Overview

This project focuses on predicting customer churn for a telecommunications company using various machine learning and deep learning models. The goal is to identify customers who are likely to leave the company and understand the key factors influencing their decisions.

Features

• Comprehensive data preprocessing and exploratory data analysis.
• Implementation of multiple machine learning models including Logistic Regression, Decision Trees, and Random Forest.
• Deep learning models like Neural Networks, CNN, RNN, and Transformer models.
• Hyperparameter tuning for model optimization.
• Model interpretation and feature importance analysis.

Dataset

The dataset used in this project is the "Telco Customer Churn" dataset, which includes customer demographic details, account information, and churn status.

Requirements

For required libraries, see requirements.txt.

Installation

Clone the repository to your local machine:

https://github.com/itsjustshubh/Telco-Customer-Churn-Prediction.git

Install the required libraries:

cd telco-customer-churn
pip install -r requirements.txt

Usage

To run the project:

1. Navigate to the cloned directory.
2. Run the main Python script:

   python churn_prediction.py

Structure

• churn_prediction.py: Main Python script with data preprocessing, modeling, and evaluation.
• data/: Directory containing the dataset file.

Contributing

Contributions to this project are welcome. Please ensure to update tests as appropriate.

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgements

• Dataset provided by Kaggle
• Dataset Link - https://www.kaggle.com/datasets/blastchar/telco-customer-churn.
• References or inspirations if any.