ML-Powered Churn Prediction
Customer Retention Analysis
Comprehensive customer churn analysis using Python and Power BI with predictive modeling achieving ~90% accuracy.
PythonPower BIMachine LearningPandasScikit-learn
~90%
Model Accuracy
Power BI
Tool
ML
Method
Problem Statement
Business needed to identify at-risk customers and understand behavioral patterns driving churn to improve retention strategies.
Architecture
Python ML pipeline for data preprocessing, feature engineering, and model training; Power BI for interactive visualization and stakeholder reporting.
Database
Structured customer transaction and behavior datasets for model training and validation.
Solution
Performed feature engineering and predictive modeling on churn data, built interactive Power BI dashboards presenting actionable retention insights.
Key Queries
- Customer behavior aggregation queries
- Churn indicator feature extraction
- Cohort retention analysis queries
Dashboards Built
- Churn prediction dashboard
- Retention trend visualizations
- Customer segment analysis panels
Business Insights
Identified key churn behavioral indicators
Enabled proactive retention interventions
Segmented customers by churn risk levels
Challenges
- Handling imbalanced churn datasets
- Feature selection for model accuracy
- Translating ML outputs to business actions
Future Scope
- Real-time churn scoring integration
- Automated retention campaign triggers
- A/B testing for retention strategies
Features
Predictive churn modelingInteractive visualizationsBehavioral trend analysisRisk segmentation