Customer Churn Prediction — Machine Learning & Deep Learning This project uses machine learning and deep learning models to predict customer churn in the telecom industry. The workflow includes:

Data preprocessing: missing value handling, categorical encoding

Model training: Logistic Regression, Decision Tree, Random Forest, XGBoost, LightGBM

Performance evaluation: accuracy, precision, recall, F1 score, and ROC-AUC metrics

Deep learning: training a simple Artificial Neural Network (ANN) with TensorFlow

Model comparison and visualization of results

Libraries Used pandas, numpy, scikit-learn

xgboost, lightgbm

tensorflow, keras

matplotlib (for plotting)

How to Run Prepare and preprocess the dataset

Train and evaluate models sequentially

Compare best-performing model with deep learning approach