Telco Customer Churn Analysis and Prediction

Table of Contents

1. Project Overview
2. Files in the Project
3. Directory Structure
4. Installation and Setup
5. Usage
6. Key Features
7. Data
8. Model Performance
9. Future Improvements
10. License
11. Contact

Project Overview

This project focuses on analyzing and predicting customer churn for a telecommunications company. It includes exploratory data analysis (EDA), model building, and a web application for making predictions.

Files in the Project

1. Churn_Analysis_EDA.ipynb: Jupyter notebook containing exploratory data analysis.
2. Churn_Analysis_Model_Building.ipynb: Jupyter notebook for building and evaluating machine learning models.
3. app.py: Flask application for deploying the model as a web service.
4. requirements.txt: List of Python dependencies for the project.

Directory Structure

• data/: Contains input data files.
• exports/: Stores output files (CSV files and model files) generated during the analysis.

Installation and Setup

To run this project, you'll need Python 3.x and the required libraries. You can install the dependencies using the following command:

pip install -r requirements.txt

This will install the following libraries:

• numpy
• pandas
• seaborn
• matplotlib
• scikit-learn
• imbalanced-learn
• flask

Usage

1. Exploratory Data Analysis:

   • Open and run Churn_Analysis_EDA.ipynb in a Jupyter environment.
   • This notebook processes the data, performs univariate and bivariate analysis, and exports the processed data to the exports/ directory.

2. Model Building:

   • Open and run Churn_Analysis_Model_Building.ipynb in a Jupyter environment.
   • This notebook builds various models including Decision Tree and Random Forest classifiers, applies SMOTEENN for handling imbalanced data, and saves the best model to the exports/ directory.

3. Web Application:

   • Run app.py to start the Flask server:

     python app.py
     

   • Access the web interface through your browser to input customer data and get churn predictions.

Usage

1. Exploratory Data Analysis:

   • Open and run Churn_Analysis_EDA.ipynb in a Jupyter environment.
   • This notebook processes the data, performs univariate and bivariate analysis, and exports the processed data to the exports/ directory.

2. Model Building:

   • Open and run Churn_Analysis_Model_Building.ipynb in a Jupyter environment.
   • This notebook builds various models including Decision Tree and Random Forest classifiers, applies SMOTEENN for handling imbalanced data, and saves the best model to the exports/ directory.

3. Web Application:

   • Run app.py to start the Flask server:

     python app.py
     

   • Access the web interface through your browser to input customer data and get churn predictions.

Key Features

• Comprehensive EDA with visualizations
• Machine learning models for churn prediction
• Handling of imbalanced dataset using SMOTEENN
• Web application for real-time predictions

Data

The project uses the Telco Customer Churn dataset. Ensure you have the following files in your data directory:

• Telco-Customer-Churn.csv
• first_telc.csv

Model Performance

The final model uses a Random Forest Classifier with SMOTEENN, achieving high accuracy and balanced performance across classes. The model file is saved in the exports/ directory.

Future Improvements

• Implement more advanced models (e.g., XGBoost, Neural Networks)
• Enhancing the web application with more features and better UI
• Incorporate more recent data for improved predictions

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any inquiries or issues, please contact the project maintainers at [ssnfs26@gmail.com].