How gyerra/5g-qos-analysis works
This is a 5G telecom QoS analytics platform. A network analyst starts with telemetry records, which the Python pipeline loads, cleans, analyzes, visualizes, and uses to train QoS prediction models; a separate PySpark path demonstrates distributed aggregation. Generated JSON results are then served by a Next.js dashboard across overview, benchmarking, prediction, and big-data views. The graph reflects documented capabilities and sampled calls; some unsampled analytics internals are represented only at their documented module paths.
Components 21
Data Pipeline
- Pipeline Runner
python/run_pipeline.py - Data Loader
python/load_data.py - Telemetry Dataset
5g_network_data.csv - Data Preprocessing
python/preprocessing/clean_data.py - Cleaned Dataset
python/data/processed/cleaned_dataset.csv
Analytics & ML
- Statistical Analysis
python/statistical_analysis.py - QoS Benchmarking
python/qos_analysis.py - EDA Visualization
python/visualization/eda.py - QoS Model Training
python/ml/train_model.py - Model Outputs
python/output/models - Output Generator
python/generate_outputs.py
Big Data
- Spark Analysis
big_data/big_data_analysis.py
Dashboard
- Dashboard JSON
public/data - Big Data View
app/bigdata/page.tsx - Overview View
app/overview/page.tsx - QoS View
app/qos/page.tsx - Prediction View
app/prediction/page.tsx - Dashboard Shell
app/layout.tsx - Sidebar Navigation
components/Sidebar.tsx - Chart Components
components/ChartCard.tsx
Other components
- Network Analyst
Connections 31
- Network Analyst → Dashboard Shell: opens dashboard
- Dashboard Shell → Sidebar Navigation: renders navigation
- Sidebar Navigation → Overview View: routes to view
- Sidebar Navigation → QoS View: routes to view
- Sidebar Navigation → Prediction View: routes to view
- Sidebar Navigation → Big Data View: routes to view
- Pipeline Runner → Data Loader: calls loader
- Pipeline Runner → Data Preprocessing: calls preprocessing
- Pipeline Runner → Statistical Analysis: calls statistics
- Pipeline Runner → QoS Benchmarking: calls benchmarking
- Pipeline Runner → EDA Visualization: calls EDA
- Pipeline Runner → QoS Model Training: calls training
- Pipeline Runner → Output Generator: calls generation
- Data Loader → Telemetry Dataset: reads records
- Data Preprocessing → Cleaned Dataset: writes cleaned data
- Statistical Analysis → Cleaned Dataset: reads cleaned data
- QoS Benchmarking → Cleaned Dataset: reads cleaned data
- EDA Visualization → Cleaned Dataset: reads cleaned data
- QoS Model Training → Cleaned Dataset: reads cleaned data
- QoS Model Training → Model Outputs: writes models
- Output Generator → Dashboard JSON: writes JSON results
- Spark Analysis → Telemetry Dataset: reads telemetry
- Overview View → Dashboard JSON: fetches summaries
- QoS View → Dashboard JSON: fetches QoS data
- Prediction View → Dashboard JSON: fetches model data
- Big Data View → Dashboard JSON: fetches Spark data
- Overview View → Chart Components: renders charts
- QoS View → Chart Components: renders charts
- Prediction View → Chart Components: renders charts
- Big Data View → Chart Components: renders charts
- Spark Analysis → Dashboard JSON: publishes results