This project aims to predict customer churn in the telecom industry using machine learning techniques. Customer churn, or the rate at which customers stop doing business with a company, is a critical metric for telecom companies as it directly impacts revenue and profitability. By accurately predicting churn, companies can take proactive measures to retain customers and improve overall business performance.
Customer churn, also known as customer attrition, is a critical challenge for telecom companies. It refers to the phenomenon where customers discontinue using the services provided by a company. High churn rates can negatively impact revenue and profitability. Therefore, predicting and reducing churn is essential for maintaining a sustainable business.
- Description of the dataset used.
- Features included and their significance.
- Distribution of key variables.
- Relationships between features and churn.
- Deriving new features.
- Selecting relevant features for modeling.
- Utilized logistic regression and random forest classifier.
- Split data into training and testing sets.
- Evaluated model performance using accuracy, precision, recall, and F1-score.
- Coefficients for logistic regression and feature importances for random forest.
- Identification of key factors predicting churn.
- Performance metrics before and after tuning.
- Improvement in model performance post-tuning.
- Recommendations based on key findings.
- Strategies to reduce churn and improve customer retention.
- Targeted marketing campaigns focusing on month-to-month contracts.
- Improving customer experience for fiber optic users.
- Enhancing security and support features.
- Addressing billing and payment issues.
- Implementing onboarding programs for new customers.