🔄 Customer Churn Classification Project

📊 Predicting Customer Churn in Telecommunication Industry

📝 Project Overview

This project focuses on predicting customer churn in the telecommunications industry using machine learning classification techniques. By analyzing historical data, we aim to identify patterns and features that can help predict customer churn.

🔧 Technologies Used

• Python: The primary programming language used for this project.
• Scikit-learn: A popular machine learning library for Python.
• Machine Learning: Classification techniques are used to predict customer churn.

📋 Project Structure

• Data: Contains the dataset used for training and testing the model.
• Notebooks: Contains Jupyter notebooks for data exploration, model training, and evaluation.
• Models: Contains the trained machine learning models: Random Forest Classifier.
• Reports: Contains project reports and analysis.

🔄 Project Steps

1. Data Collection: Gather historical customer data from the telecommunications industry.
2. Data Preprocessing: Clean and preprocess the data for analysis.
3. Exploratory Data Analysis: Perform exploratory data analysis to understand the data.
4. Feature Engineering: Create relevant features for the machine learning model.
5. Model Training: Train machine learning models on the preprocessed data.
6. Model Evaluation: Evaluate the performance of the trained models.
7. Model Deployment: Deploy the best-performing model for customer churn prediction.

📄 License

This project is licensed under the MIT License.

📚 References

• Scikit-learn Documentation
• Python Documentation

📧 Contact

If you have any questions or need further assistance, please contact the project maintainer. edgarabasov1@gmail.com

📄 Acknowledgments

Dataset is taken form Kaggle.

📅 Last Updated

This project was last updated on 16/11/2024.

🌐 Website

For more information, please visit our website at https://customer-churn-classification.streamlit.app/.