Customer churn prediction #205
Description
🔍 Problem Description:
Customer churn prediction helps businesses, especially in subscription-based sectors like telecom, to identify customers who may cancel their service. By predicting churn, companies can proactively take measures to retain customers, thereby reducing revenue loss and enhancing customer satisfaction.
🧠 Model Description:
This project utilizes a Random Forest Classifier for binary classification, which is effective for structured data and allows us to evaluate feature importance. Random Forest is suitable for this problem because it handles various customer data types (e.g., demographics, account details, and service usage) and helps identify key factors contributing to churn. The model provides a clear, interpretable outcome, highlighting the primary attributes associated with churn behavior.
⏲️ Estimated Time for Completion:
The project is expected to take approximately 1-2 hours, covering data preprocessing, model training, evaluation, and generating documentation.
🎯 Expected Outcome:
The trained model will classify whether a customer is likely to churn with a target accuracy of around 80-85%. Key metrics such as precision, recall, and F1-score will provide insight into the model’s performance. Additionally, feature importance analysis will reveal which factors are most strongly associated with churn, aiding in targeted retention strategies.
📄 Additional Context:
This project uses the Telco Customer Churn Dataset, which includes various customer demographics, service usage information, and account characteristics. This initial implementation focuses on simplicity, efficiency, and interpretability. Future improvements could explore alternative algorithms or fine-tuning for enhanced performance.
To be Mentioned while taking the issue:
- Participant Role: (e.g., Hacktoberfest, GSSOC, SSOC, etc.)
- "Contributor under Hacktoberfest and GSSOC'Extd"
Note:
- Please review the project documentation and ensure your code aligns with the existing structure.
- Ensure that either the
predict.pyfile or the notebook includes amodel_details()function that provides a detailed model report, which is essential for generating model metrics and is required for acceptance. - Prefer using a separate branch to manage changes, maintaining stability in the main branch.
- Follow the repository's pull request template when creating and submitting a pull request.