Project-2: Customer Churn Prediction Model
This repository contains a machine learning model for predicting customer churn in a business.
Motivation
The purpose of this project is to develop a model that can predict customer churn, helping businesses take proactive measures to retain customers and reduce revenue loss.
Success Metrics
The success of the model will be evaluated based on accuracy, precision, recall, and F1 score.
Requirements & Constraints
Functional Requirements
- Access to historical customer data.
- Python environment with necessary libraries.
- Data preprocessing scripts.
Non-Functional Requirements
- Performance, accuracy, and maintainability.
- Well-structured and documented code.
Constraints
- Structured customer data only.
- Model accuracy depends on input data quality.
Out-of-Scope
- Real-time prediction.
- External factors influencing churn.
Methodology
Problem Statement
Predict customer churn as a binary classification problem.
Data
Historical customer information, including usage patterns, purchase history, and feedback.
Techniques
Utilize classification algorithms (e.g., logistic regression, random forests) and feature engineering.
Architecture
- Data Collection
- Data Preprocessing
- Feature Engineering
- Model Training
- Model Evaluation
Conclusion
This churn prediction model aids businesses in identifying potential churners. The model’s success hinges on accurate data and relevant features. Future improvements could include real-time deployment and external factor consideration.
- Can you Explore more about the project refer the GitHub Link