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.

• 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.

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

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

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.

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