Customer Churn Prediction

Problem Statement

Customer churn refers to when customers stop using company services. In this project, we aim to predict customer churn using a historical dataset of customer behavior. This prediction helps businesses take proactive measures to retain valuable customers.

Dataset Description

The dataset consists of customer demographic details, account information, services subscribed, and churn status. The target variable is Churn, where Yes means the customer has churned, and No means they have not.

Columns:

• gender, SeniorCitizen, Partner, Dependents, etc. (Demographics)
• tenure, PhoneService, InternetService, etc. (Account info)
• Churn (Target Variable)

Objective

Our goal is to develop a machine learning model that accurately predicts whether a customer will churn or not. We use Logistic Regression as the base model and tune hyperparameters using GridSearchCV.

Approach

1. Data Cleaning and Preprocessing
2. Feature Encoding and Scaling
3. Logistic Regression Model with Hyperparameter Tuning
4. Model Evaluation
5. Visualization of Key Correlations and Features

Conclusion

• The best model achieved an accuracy of X% (replace with actual score).
• tenure, MonthlyCharges, and contract types were highly influential in predicting churn.