Customer Churn Prediction in A Telecommunications Company - A Machine Learning Classification Project
This repository contains a machine learning project focused on predicting customer churn in the telecommunications industry. Customer churn refers to the phenomenon where customers discontinue using a company's services. Understanding and predicting churn is crucial for telecom companies to retain customers and maintain profitability.
| Jupyter Notebook | Published Article | PowerBi Dashboard Deployment |
|---|---|---|
| Notebook with code and full analysis | Published Article | PowerBI Dashboard |
The goal of this project is to develop a predictive model that can accurately identify customers who are likely to churn. By analyzing a comprehensive dataset provided by a telecom company, I aim to uncover patterns, relationships, and key factors influencing churn. The project involves data exploration, preprocessing, feature engineering, model development, evaluation, and interpretation.
Exploratory Data Analysis (EDA): Gain insights into the dataset and understand the distribution, relationships, and characteristics of the variables.
Missing Value Handling: Address missing values in the dataset using appropriate imputation or removal techniques.
Feature Engineering: Transform and create new features to enhance the predictive power of the models.
Model Development: Utilize various machine learning algorithms to develop predictive models for customer churn.
Model Evaluation: Assess the performance of the developed models using appropriate evaluation metrics.
Model Interpretation: Interpret the models to understand the factors driving customer churn and their relative importance.
To get started with this project, follow these steps:
Clone the repository to your local machine. Install the required dependencies and libraries. Open the Jupyter notebooks in the notebooks/ directory. Execute the code cells in sequential order, following the instructions provided. Explore the results, analysis, and interpretations in the notebooks. Modify and experiment with the code as needed to further enhance the project.
Contributions to this project are welcome! If you have any ideas, suggestions, or improvements, please feel free to open an issue or submit a pull request.
This project is licensed under the MIT License.
Note: Please note that this project is for educational and research purposes only, and the insights and predictions should be interpreted with caution in real-world applications.