How pranav-git27/uidai-project works
UIDAI Analytics is a prototype decision-support dashboard for exploring anonymized enrolment and update activity. Stakeholders select state and date filters, then view KPIs, trends, anomaly findings, forecasts, or filtered raw records. The two training scripts build anomaly-detection and demand-forecasting models from the synthetic dataset; the dashboard loads persisted artifacts for inference. Forecast training uses the shared data loader, while anomaly training reads the CSV directly. Dashboard capabilities are documented in the README, but omitted source lines limit confirmation of their internal wiring.
Main entry points: dashboard.py Dashboard App · train_forecast.py Forecast Training · uidai_analytics_synthetic_data.csv Synthetic Dataset · train_anomaly.py Anomaly Training
Components 19
Dashboard
- Dashboard App
app/dashboard.py - KPIs
app/dashboard.py - Trend Analysis
app/dashboard.py - Anomaly Analysis
app/dashboard.py - Forecast View
app/dashboard.py - Raw Data View
app/dashboard.py
Data
- Synthetic Generator
data/generate_synthetic_data.py - Synthetic Dataset
data/uidai_analytics_synthetic_data.csv - Data Loader
utils/data_loader.py
Model Training
- Anomaly Training
ml/train_anomaly.py - Forecast Training
ml/train_forecast.py - Model Utilities
utils/model_utils.py
Model Artifacts
- Anomaly Model
models/anomaly_model.pkl - Label Encoders
models/Label_Encoders.pkl - Forecast Ensemble
models/forecasting_ensemble.pkl - Sample Forecasts
models/sample_forecasts.pkl - Feature Scaler
models/feature_scaler.pkl - Feature List
models/feature_list.pkl
Other components
- Government Stakeholder
Connections 23
- Government Stakeholder → Dashboard App: uses
- Synthetic Generator → Synthetic Dataset: writes
- Dashboard App → Synthetic Dataset: loads data
- Forecast Training → Data Loader: loads data
- Data Loader → Synthetic Dataset: reads
- Anomaly Training → Synthetic Dataset: reads
- Anomaly Training → Anomaly Model: saves model
- Anomaly Training → Label Encoders: saves encoders
- Forecast Training → Model Utilities: uses utilities
- Forecast Training → Forecast Ensemble: saves ensemble
- Forecast Training → Sample Forecasts: saves forecasts
- Forecast Training → Feature Scaler: saves scaler
- Forecast Training → Feature List: saves features
- Dashboard App → Anomaly Model: loads model
- Dashboard App → Label Encoders: loads encoders
- Dashboard App → Forecast Ensemble: loads model
- Dashboard App → Feature Scaler: loads scaler
- Dashboard App → Feature List: loads features
- Dashboard App → KPIs: calculates
- Dashboard App → Trend Analysis: renders
- Dashboard App → Anomaly Analysis: detects and presents
- Dashboard App → Forecast View: presents forecasts
- Dashboard App → Raw Data View: presents records