Customer Churn Prediction

🚀 Diving into Deep Learning: Building My First ANN Model for Customer Churn Prediction! I'm excited to share that I've successfully completed my first project where I built an Artificial Neural Network (ANN) model to tackle a real-world problem: predicting customer churn! 🌟 Project Problem Statement: In the competitive banking industry, retaining customers is crucial. The project involved building a model to predict whether a customer will leave the bank based on factors like credit score, age, balance, and more. By accurately predicting customer churn, banks can proactively address customer concerns and improve retention strategies. How This Helps in the Real World: Customer churn prediction is vital for businesses, especially in sectors like banking, telecom, and retail. With this model, banks can identify at-risk customers and take actions to retain them, thereby reducing loss and improving customer satisfaction. This approach is not only efficient but also scalable, allowing businesses to stay ahead in a competitive market. What I Learned: - Model Architecture: Developed an ANN model from scratch, learning the intricacies of designing effective neural networks for classification. - Data Preprocessing: Mastered techniques for preparing data, including normalization and encoding, to enhance model performance. - Optimization Techniques: Experimented with different optimizers and activation functions to fine-tune the model's accuracy. - Model Evaluation: Gained experience in evaluating model performance using metrics like accuracy, loss, and confusion matrices. This project was an incredible learning experience and has laid the foundation for my future endeavors in deep learning and AI. I'm excited to continue exploring this field and applying these techniques to more complex problems. #DeepLearning #ArtificialNeuralNetwork #MachineLearning #DataScience #AI #CustomerChurn #LearningJourney #Python #TensorFlow