This project aims to predict customer churn using Artificial Neural Networks (ANN) and the sklearn library.
Customer churn refers to the phenomenon where customers stop doing business with a company. Predicting customer churn is crucial for businesses to retain their customers and improve customer satisfaction. In this project, we use ANN to predict customer churn based on various features.
The dataset used for this project contains information about customers, including demographic data, usage patterns, and customer churn status. The dataset can be found here.
To run this project, you need to have the following dependencies installed:
- Python 3.x
- tensorflow
- scikit-learn
- numpy
- pandas
- matplotlib
You can install the required dependencies by running the following command:
pip install -r requirements.txt
To use this project, follow these steps:
- Clone the repository or download the project files.
- Open the project in Jupyter Notebook.
- Run the notebook cells sequentially to preprocess the data, train the ANN model, and make predictions.
- Analyze the results and evaluate the performance of the model.
Make sure to update the file paths and dataset location according to your setup.
The results of the customer churn prediction will be displayed in the notebook and can be further analyzed using various evaluation metrics.
Contributions to this project are welcome. If you find any issues or have suggestions for improvement, please open an issue or submit a pull request.