customer-churn
Project Land o’ Lakes
Customer Churn Prediction 📉
Customer churn is one of the most critical business metrics, directly impacting revenue and long-term growth. This project leverages Artificial Intelligence and Machine Learning to predict the likelihood of customer churn, enabling businesses to take proactive measures to retain customers.
Value Proposition🪙
- Quality: Provides accurate churn predictions using advanced ML algorithms.
- Reduced Costs: Retaining existing customers is cheaper than acquiring new ones.
- Actionable Insights: Helps businesses understand key drivers of churn. High churn often indicates problems with customer satisfaction, product value, or service quality.
- Revenue Impact: High churn reduces recurring income. Improves customer retention, increasing lifetime value and profitability.
Table of Contents📖
- Value Proposition
- Features
- Tech Stack
- Prerequisites
- How to Use
- Enterprise Use Cases
- License
- Contact
- Acknowledgements
Features✨
- Data Preprocessing: Automatic handling of missing values and categorical encoding.📊
- Exploratory Analysis: Insights into churn patterns and influential features.🔍
- Machine Learning Model: Logistic Regression with One-Hot Encoding and Imputation.🧠
- Interactive Input: Users can enter customer data to get real-time churn predictions.⚡
- Probability Scores: Provides churn likelihood (%) for actionable decisions.📈
- Scalable Pipeline: Easily extendable to other ML models (Random Forest, XGBoost, etc.).🔒
Tech Stack🛠
- Languages: Python, Bash
- Libraries: NumPy, Pandas, PyTorch, Scikit-learn, TensorFlow, XGBoost
- DevOps: GitHub
- UX/UI Visualization: CSS3, HTML5, Kotlin, Matplotlib, Plotly, Seaborn, Swift
Prerequisites🤖
Python 3.10+
How to Use 📈
User Input
- Age: 32
- Gender: Male
- Tenure: 12
- Usage Frequency: 20
- Support Calls: 3
- Payment Delay: 2
- Subscription Type: Premium
- Contract Length: Annual
- Total Spend: 1200.50
- Last Interaction: 5
Output
- Prediction: Stay
- Probability of Churn: 18.73%
Enterprise Use Cases📈
- Telecom: Predict customer contract cancellations. 📡
- Streaming Services: Identify subscribers likely to cancel Netflix/Spotify. 🎵
- E-commerce: Spot shoppers likely to abandon future purchases.🛒
- Banking & Finance: Detect clients at risk of closing accounts.🏦
- SaaS Companies: Forecast subscription non-renewals.💻
License⚖️
This project is licensed under a dual license. You are free to use, modify, and distribute this software with attribution for personal or research use, but must open source modifications to the community. Integration into closed source, commercial proprietary systems requires a low cost annual subscription and license fees. See license.txt for details.
Contact📬
Acknowledgements🙏
- Scikit-learn for robust AI/ML utilities
- UC Irvine, Kaggle, and other datasets for training data
- Open-source contributors and the SWE research community
- Inspiration from industry leaders in AI/ML models
- I am grateful. Thank you!