Customer Churn Prediction Model

1. Overview

This repository contains a machine learning model for predicting customer churn in a business. Churn prediction is the process of identifying customers who are likely to stop using a product or service. The model utilizes historical customer data and applies various machine learning techniques to make accurate predictions about potential churners.

2. Motivation

The primary motivation behind developing this churn prediction model is to help businesses retain customers more effectively. By identifying customers at risk of churning, businesses can take proactive measures to address their concerns, improve customer satisfaction, and reduce revenue loss due to customer attrition.

3. Success Metrics

The success of the model will be measured based on the following metrics:

• Accuracy: The proportion of correctly predicted churn and non-churn instances.
• Precision: The ability to correctly identify churners among the predicted positives.
• Recall: The ability to correctly identify all actual churners.
• F1 Score: The harmonic mean of precision and recall, providing a balanced performance measure.

4. Requirements & Constraints

4.1 Functional Requirements

• Access to historical customer data, including features relevant to churn prediction.
• Python environment with necessary libraries (scikit-learn, pandas, etc.).
• Data preprocessing scripts to clean and prepare the data.

4.2 Non-Functional Requirements

• Performance: The model should provide predictions within a reasonable time frame, considering the data size.
• Accuracy: The model should achieve a competitive accuracy score compared to existing solutions.
• Maintainability: Code should be well-structured, documented, and modular for easy maintenance and updates.

4.3 Constraints

• Limited to using structured customer data for prediction.
• The model's accuracy is dependent on the quality and relevance of the input data.

4.4 Out-of-Scope

• Real-time prediction is out of scope for this version.
• External factors affecting churn (e.g., economic conditions) are not considered.

5. Methodology

5.1 Problem Statement

The goal is to predict whether a customer will churn or not based on historical data and relevant features. This is a binary classification problem.

5.2 Data

The dataset consists of historical customer information, including attributes such as usage patterns, purchase history, customer feedback, etc.

5.3 Techniques

The model will use various classification algorithms such as logistic regression, random forests, and gradient boosting. Feature engineering techniques will be applied to extract relevant information from the data.

6. Architecture

The architecture includes:

• Data collection and preprocessing.
• Feature engineering and selection.
• Model training and validation.
• Model evaluation and selection.

7. Pipeline

1. Data Collection: Gather historical customer data from various sources.
2. Data Preprocessing: Clean the data, handle missing values, and perform necessary transformations.
3. Feature Engineering: Create new features and select relevant ones.
4. Model Selection: Train multiple classification models using the preprocessed data.
5. Model Evaluation: Evaluate models using cross-validation and select the best-performing one.
6. Model Deployment (Future Scope): Deploy the chosen model for making predictions.

8. Conclusion

Customer churn prediction is a critical task for businesses to retain customers and enhance customer satisfaction. This repository provides a churn prediction model that leverages machine learning techniques to help businesses identify potential churners and take proactive measures. The success of the model will be determined by its accuracy, precision, recall, and F1 score. It's important to note that the model's effectiveness heavily relies on the quality of input data and the chosen features. Further improvements and real-time deployment could be explored in future iterations of the project.