Predicting Customer Churn for a Telecom Company

Problem Statement:

A telecom company wants to predict whether a customer will churn (leave the service) based on their usage patterns and account information. The company would like to use this model to identify customers who are likely to churn and target them with retention offers.

Step 1: Problem Understanding

• Goal: Predict whether a customer will churn based on features like monthly charges, contract type, and customer service calls.
• Target variable: Churn (binary, 1 if customer churns, 0 if they stay).
• Key business outcomes: Reduce churn by proactively reaching out to at-risk customers.

Step 2: Data Collection

Step 3: Data Preprocessing and Exploration

Step 4: Feature Engineering

Step 5: Data Splitting

Step 6: Model Selection

Step 7: Model Evaluation

Step 8: Model Tuning

Step 9: Model Deployment Using Flask

Step 10: Model Monitoring and Retraining