rubinraza/customer-churn-predictor

Customer Churn Prediction using TensorFlow/Keras

1. Project Overview This project uses an Artificial Neural Network (ANN) to predict customer churn based on a dataset of telecom customers. The goal is to identify users who are likely to cancel their service so the business can take action to keep them.

2. Model Architecture I built a Sequential model with three layers:

Input Layer: Processes the customer features (tenure, monthly charges, etc.).

Hidden Layers: Two layers with 32 and 64 neurons using the ReLU activation function to find patterns.

Output Layer: A single neuron with a Sigmoid activation to output a probability between 0 and 1.

3. Training & Performance Optimizer: Stochastic Gradient Descent (SGD).

Loss Function: Binary Crossentropy.

Epochs: 200.

Final Accuracy: 78.1% on the test dataset.

4. How to Use To use this model, you will need TensorFlow installed. You can load the my_model.keras file using the following Python code:

Python from tensorflow.keras.models import load_model model = load_model('my_model.keras')