Live Demo: [Deploy your Streamlit app and add URL here]
Professional customer churn prediction system for telecommunications companies. Combines advanced machine learning with interactive business intelligence dashboard powered by VL Analytics.
- 84.9% Accuracy using Logistic Regression model
- Interactive Dashboard with real-time insights
- Risk Assessment tools for customer segmentation
- Strategic Recommendations for retention campaigns
- Professional Branding with VL Analytics design
- Identifies at-risk customers with high precision
- Provides actionable retention strategies
- Reduces churn through targeted interventions
- Quantifies revenue at risk ($1.45M+ identified)
- Frontend: Streamlit
- ML/Data: Scikit-learn, Pandas, NumPy
- Visualization: Plotly, Seaborn, Matplotlib
- Deployment: Streamlit Community Cloud
# Clone repository
git clone [your-repo-url]
# Install dependencies
pip install -r requirements.txt
# Run dashboard
streamlit run app_vl_analytics.pyTelco Customer Churn Dataset - 7,043 customers with 21 features including demographics, services, and billing information.
Customer_Churn_Prediction/
├── app_vl_analytics.py # Main dashboard application
├── requirements.txt # Dependencies
├── data/ # Dataset and processed files
├── models/ # Trained ML models
├── outputs/ # Visualizations and results
├── VL_Analytics_Branding/ # Professional branding assets
└── README.md # This file
- Accuracy: 84.9%
- Precision: High-risk customer identification
- Business Impact: $1.45M+ revenue at risk identified
- Deployment: Production-ready Streamlit interface
"We Shall See the Light" - Professional data science consultancy transforming complex data into enlightened business decisions through ethical AI and advanced analytics.
Core Services:
- Predictive Analytics & Machine Learning
- Business Intelligence Dashboards
- Data Strategy Consulting
- AI-Driven Insights
© 2025 Victor Collins Oppon | Vidibemus Lumen Analytics
Built for data-driven decision making