Telecom Customer Churn Prediction

This project aims to predict customer churn in a telecom company using machine learning techniques. Customer churn, also known as customer attrition, refers to the phenomenon where customers cease doing business with a company. Predicting churn is crucial for businesses as it allows them to take proactive steps to retain customers.

Table of Contents

• Overview
• Project Overview
• Dataset
• Features
• Usage
• Dependencies
• Installation
• Data Preprocessing
• Model Building
• Evaluation
• Deployment
• Contributing
• License

Overview

In this project, we utilize machine learning techniques to build a predictive model for customer churn. The model is trained on historical data that includes information about telecom customers and whether they churned or not.

Project Overview

• Problem Statement: Predict telecom customer churn using machine learning.
• Technologies Used: Python, scikit-learn, pandas, Flask (for deployment).
• Model: We trained and evaluated multiple machine learning models (e.g., logistic regression, random forest, XGBoost) and selected the best-performing one.
• Evaluation Metrics: We used accuracy, precision, recall, F1-score, and ROC-AUC to assess model performance.

Dataset

• We used the Telecom Customer Churn dataset for this project. This dataset contains information about customer demographics, usage patterns, and whether they churned or not.

Usage

To use this project, follow these steps:

1. Clone the repository to your local machine.
2. Install the required dependencies (see Dependencies).
3. Follow the Installation instructions.
4. Train the model using Model Training instructions.
5. Evaluate the model using Evaluation instructions.

Installation

Mac OS

1. Clone the repository:

git clone https://github.com/your-username/telecom-churn-prediction.git

2. Navigate to the project directory:

cd Customer-Churn`

3. Create virtual environment

python3 -m venv venv

4. Activate Virtual Environment

source venv/bin/activate

5. Install the required dependencies:

pip install -r requirements.txt

Windows

1. Clone the repository:

git clone https://github.com/your-username/telecom-churn-prediction.git

2. Navigate to the project directory:

cd Customer-Churn`

3. Create virtual environment

python -m venv venv

4. Activate Virtual Environment

venv\Scripts\activate

5. Install the required dependencies:

pip install -r requirements.txt

Dependencies

• Python 3.7+
• scikit-learn
• pandas
• Flask
• numpy
• matplotlib
• seaborn
• plotly

Data Preprocessing

• Exploratory Data Analysis (EDA): We performed data visualization and analysis to gain insights into the dataset.
• Data Cleaning: Handled missing values, outliers, and duplicate records.
• Feature Engineering: Created new features and transformed existing ones.
• Data Encoding: Encoded categorical variables and scaled numerical features.

Model Building

• We trained several machine learning models, including logistic regression, random forest, and XGBoost.
• Used cross-validation for hyperparameter tuning and model selection.
• Saved the best-performing model for deployment.

Evaluation

• Evaluated model performance using various metrics, including accuracy, precision, recall, F1-score, and ROC-AUC.
• Created visualizations to showcase results.
• Compared the model's performance against a baseline.

Deployment

• Deployed the best model using a Flask web application.
• Created an API endpoint for making predictions.
• Hosted the application on a cloud server (e.g., Heroku).

Contributing

Contributions to this project are welcome. To contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Create a pull request.

License

This project is licensed under the MIT License.