Customer Churn Prediction

This project focuses on predicting customer churn in the mobile telecommunications industry using machine learning techniques. The goal is to identify customers who are likely to stop using a service, allowing businesses to take proactive measures to retain them.

Features

• Data Analysis: Exploration of customer behavior data to identify patterns and trends.
• Model Training: Implementation of various machine learning models to predict churn.
• Evaluation: Assessment of model performance using accuracy, precision, recall, and other metrics.

Technologies Used

• Jupyter Notebook: For data analysis and model development.
• Python: Core programming language.
• Machine Learning Libraries: scikit-learn, pandas, NumPy.

Dataset

The project uses a dataset that includes customer information such as usage patterns, subscription details, and demographics.

Getting Started

1. Clone the repository:

   git clone https://github.com/shalindasilva1/Customer-churn-prediction.git

2. **Install the required dependencies:

   pip install -r requirements.txt

3.Run the Jupyter notebook to explore the data and train models.

License

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