# Introduction
This document outlines the requirements for developing, training, and deploying a machine learning model to predict customer churn.

The goal is to create a predictive model to forecast customer churn in a subscription-based service, enabling targeted retention strategies.

# Data Requirements
Sources: Customer databases, transaction logs, CRM systems
Types: Structured (e.g., demographics) and unstructured data (e.g., support interactions).
Preprocessing: Handle missing values, normalize, scale, encode, and split data (70/20/10 for training, validation, and test sets).
Volume: Minimum 100,000 records stored securely with encryption.

# Training and Evaluation
Environment: Use HPC clusters or cloud-based solutions (AWS, GCP, Azure) with software like Python, TensorFlow, PyTorch, Scikit-learn.
Hyperparameter Tuning: Methods include Grid Search and Random Search, tuning parameters like learning rate, number of trees, and epochs.
Process: Multiple iterations with continuous validation to refine model performance.
Metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC.
