Predictive Analytics Dashboards
Build predictive analytics dashboards with ML models for forecasting, churn prediction, demand planning, and anomaly detection. Include interactive visualizations and what-if scenario analysis.
Project Milestone & Feature Breakdown
1 Data Pipeline & Feature Engineering
Prepare data for ML models
8 pts 1-2 weeks 2 Features
Data Pipeline & Feature Engineering
Prepare data for ML models
Data Ingestion
Collect and aggregate historical data
Feature Engineering
Create features for ML models
Deliverables
- Data pipeline
- Features
- Training dataset
2 ML Models Development
Train and deploy predictive models
13 pts 2-3 weeks 3 Features
ML Models Development
Train and deploy predictive models
Forecasting Models
Time series forecasting (ARIMA, Prophet, LSTM)
Churn Prediction
Predict customer churn probability
Anomaly Detection
Detect unusual patterns and outliers
Deliverables
- Trained models
- Model evaluation
- Deployment pipeline
3 Visualization & Dashboard
Interactive analytics dashboards
8 pts 1-2 weeks 2 Features
Visualization & Dashboard
Interactive analytics dashboards
Forecast Visualization
Charts with confidence intervals
What-If Analysis
Interactive scenario modeling
Deliverables
- Interactive dashboards
- What-if tools
- Alerts
Technical Stack
Key Considerations
Model accuracy and validation
Retraining frequency
Explainability
Data quality
Compute resources
Success Criteria
Forecasts are accurate (low MAPE)
Churn predictions actionable
Anomalies detected early
Dashboards interactive
Models retrain automatically
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