Customer Churn Prediction

This project focuses on predicting customer churn using Artificial Neural Networks (ANN) in deep learning. It was implemented as part of an assignment while learning deep learning concepts, providing extensive hands-on experience with the TensorFlow library.

Project Overview

Customer churn prediction is a critical task for businesses to retain their customers. By predicting which customers are likely to leave, companies can take proactive measures to improve customer satisfaction and loyalty.

Implementation

The project utilizes an Artificial Neural Network (ANN) to predict customer churn. The implementation involves the following steps:

1. Data Preprocessing: Cleaning and preparing the dataset for training.
2. Model Building: Constructing the ANN architecture using TensorFlow.
3. Model Training: Training the ANN model on the preprocessed data.
4. Evaluation: Assessing the model's performance using various metrics.
5. Prediction: Using the trained model to predict customer churn.

Tools and Libraries

• TensorFlow: Used for building and training the ANN model.
• Pandas: For data manipulation and preprocessing.
• NumPy: For numerical operations.
• Matplotlib: For data visualization.

Learning Outcomes

Through this project, I gained practical experience in:

• Building and training neural networks using TensorFlow.
• Preprocessing data for machine learning tasks.
• Evaluating model performance and making predictions.
• Understanding the importance of customer churn prediction in business.

Conclusion

This project was a valuable learning experience in deep learning and neural networks. It provided hands-on practice with TensorFlow and reinforced the concepts learned during the course.

Acknowledgements

I would like to thank my instructors and peers for their support and guidance throughout this assignment.

Contact

1. Email :- harshbhanushali.ai@gamil.com
2. Linkdin :- Linkdin Profile