Customer Churn Prediction Model

Overview

This project implements a machine learning solution to predict customer churn using various classification algorithms. The model helps identify customers who are likely to discontinue services, enabling proactive retention strategies.

Features

• Data preprocessing and exploratory data analysis
• Implementation of multiple machine learning models:
  • XGBoost Classifier
  • Random Forest Classifier
  • Decision Tree Classifier
  • Support Vector Machine (SVM)
  • K-Nearest Neighbors (KNN)
  • Neural Network
• Model evaluation and comparison
• Interactive web interface using Streamlit
• Model persistence using pickle
• Handling imbalanced data using SMOTE

Installation

Prerequisites

• Python 3.11
• Conda (recommended for environment management)

Environment Setup

Create a new conda environment

conda create -n churn-prediction python=3.11

Activate the environment

conda activate churn-prediction

Install required packages

pip install -r requirements.txt

Required Packages

• numpy
• pandas
• scikit-learn
• streamlit
• xgboost
• imbalanced-learn
• python-dotenv
• matplotlib
• seaborn
• plotly

Usage

Running the Jupyter Notebook

jupyter notebook churn.ipynb

Running the Streamlit App

streamlit run main.py

Project Structure

customer-churn/ ├── churn.ipynb # Main notebook with model development ├── main.py # Streamlit application ├── churn.csv # Dataset ├── models/ # Saved model files │ ├── dt_model.pkl │ ├── knn_model.pkl │ ├── rf_model.pkl │ ├── svm_model.pkl │ └── xgb_model.pkl ├── requirements.txt # Project dependencies └── README.md # Project documentation

Model Performance

The project implements and compares several machine learning models for churn prediction. Each model is evaluated using metrics such as accuracy, precision, recall, and F1-score.

Environment Variables

Create a .env file in the project root with the following variables:

GROQ_API_KEY=<your-groq-api-key>

Contributing

1. Fork the repository
2. Create a new branch
3. Make your changes
4. Submit a pull request

License

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

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