AI-Powered Fault Prediction in 5G Testbed

📋 Project Overview

An AI-powered network management system that predicts faults in 5G testbed environments using machine learning. This project aims to proactively detect and prevent network failures, improving service quality and reducing downtime.

---

👥 Team Structure

Member	Role	Responsibilities
Member 1	Data Engineer	Dataset creation, preprocessing, and validation
Member 2	ML Engineer	Model training, optimization, and evaluation
Member 3	Backend Developer	API development and ML model integration
Member 4	Frontend Developer	Dashboard creation and visualization

---

📁 Project Structure

AI-powered-fault-prediction/
│
├── data/                          # Dataset storage
│   └── synthetic_5g_fault_dataset.csv
│
├── scripts/                       # Data generation & preprocessing scripts
│   ├── generate_synthetic_data.py
│   └── data_preprocessing.py (Day 2)
│
├── notebooks/                     # Jupyter notebooks for analysis
│   └── eda_report.ipynb (Day 3)
│
├── models/                        # Trained ML models
│   └── fault_prediction_model.pkl
│
├── api/                          # Backend API code
│   └── app.py
│
├── dashboard/                    # Frontend dashboard
│   └── streamlit_app.py
│
├── requirements.txt              # Python dependencies
└── README.md                     # Project documentation

---

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

1. Navigate to the project directory:

cd AI-powered-fault-prediction

2. Install dependencies:

pip install -r requirements.txt

3. Generate and preprocess data (Days 1-2 Completed ✅):

cd scripts
python generate_synthetic_data.py
python data_preprocessing.py

Processed Data Ready for ML Training

• data/train.csv - 8,000 samples for training
• data/test.csv - 2,000 samples for testing
• data/scaler.pkl - StandardScaler for deployment
• data/label_encoder.pkl - Label encoder for predictions

---

📊 Dataset Information

Processed Dataset Ready for ML

Training Set: data/train.csv

• Samples: 8,000
• Features: 17 (scaled and encoded)
• Class Distribution: 70.6% Faulty, 29.4% Normal

Test Set: data/test.csv

• Samples: 2,000
• Features: 17 (scaled and encoded)
• Class Distribution: 70.7% Faulty, 29.3% Normal

Original Dataset: data/synthetic_5g_fault_dataset.csv (10,000 samples)

Features (19 total)

Network Performance Metrics

• rssi_dbm: Received Signal Strength Indicator (dBm)
• sinr_db: Signal-to-Interference-plus-Noise Ratio (dB)
• throughput_mbps: Data throughput (Mbps)
• latency_ms: Network latency (milliseconds)
• jitter_ms: Packet delay variation (milliseconds)
• packet_loss_percent: Packet loss percentage

Infrastructure Metrics

• cpu_usage_percent: CPU utilization
• memory_usage_percent: Memory utilization
• temperature_celsius: Equipment temperature
• active_users: Number of connected users

Contextual Features

• timestamp: Time of measurement
• base_station_id: Base station identifier
• cell_id: Cell tower identifier
• hour: Hour of day (0-23)
• day_of_week: Day of week (0-6)
• is_peak_hour: Peak hour indicator (9 AM - 5 PM)

Derived Features

• network_quality_score: Composite network health metric (0-1)
• resource_stress: Average CPU and memory utilization

Target Variable

• fault_status: Normal or Faulty

---

📅 Development Timeline

✅ Day 1 - Dataset Creation (Completed)

• Synthetic dataset generation with 10,000 samples
• 19 features including network metrics and fault labels
• Data validation (5/5 checks passed)
• Deliverables: synthetic_5g_fault_dataset.csv, generate_synthetic_data.py

✅ Day 2 - Data Preprocessing (Completed)

• Data cleaning and validation
• Feature scaling (StandardScaler) and encoding
• Train-test split (80-20, stratified)
• Saved preprocessing artifacts
• Deliverables: data_preprocessing.py, train.csv (8K), test.csv (2K), scaler.pkl, label_encoder.pkl

✅ Day 3 - Exploratory Data Analysis (Completed)

• Feature distribution analysis
• Correlation analysis and heatmap
• Class balance visualization
• Feature importance identification
• Temporal pattern analysis
• Deliverables: eda_report.ipynb with 15+ visualizations

✅ Day 4 - Documentation & Handoff (Completed)

• Final dataset documentation
• Model training guidelines and sample code
• API integration specifications
• Complete ML team handoff documentation
• Deliverables: HANDOFF_TO_ML_TEAM.md - Complete guide for ML Engineer

---

🔧 Usage

Data Pipeline (Completed ✅)

# Generate dataset
cd scripts
python generate_synthetic_data.py

# Preprocess data
python data_preprocessing.py

Next Steps for ML Engineer (Member 2)

import pandas as pd
import pickle

# Load preprocessed data
train_df = pd.read_csv('data/train.csv')
test_df = pd.read_csv('data/test.csv')

# Load scaler and encoder for deployment
with open('data/scaler.pkl', 'rb') as f:
    scaler = pickle.load(f)

with open('data/label_encoder.pkl', 'rb') as f:
    label_encoder = pickle.load(f)

# Features and target
X_train = train_df.drop('fault_status', axis=1)
y_train = train_df['fault_status']

# Start model training...

---

📈 Model Development (Member 2)

The preprocessed data will be used to train:

• Random Forest Classifier
• XGBoost
• Support Vector Machine (SVM)
• Neural Networks

Evaluation Metrics:

• Accuracy
• Precision
• Recall
• F1-Score
• ROC-AUC

---

🌐 API Development (Member 3)

Backend API will provide:

• /predict - Real-time fault prediction
• /upload - Bulk data upload
• /health - System health check
• /metrics - Network metrics dashboard

---

📊 Dashboard Features (Member 4)

Interactive dashboard will display:

• Real-time network health status
• Fault probability visualization
• Alert notifications
• Historical trend analysis
• Network KPI monitoring

---

🤝 Contributing

Each team member works on their designated area:

1. Create feature branch from main
2. Commit changes with clear messages
3. Test thoroughly before merge
4. Document all changes

---

📝 License

This is an academic project for 5G network fault prediction research.

---

📞 Contact

Team Members:

• Data Engineer: Dataset & Preprocessing
• ML Engineer: Model Development
• Backend Developer: API Integration
• Frontend Developer: Dashboard & UI

---

Last Updated: November 4, 2025
Status: Days 1-4 Complete ✅ | Data Engineering Finished | Ready for ML Training 🚀

---

🎯 Data Engineering Complete!

All data work is finished! The ML team has everything needed:

• ✅ Clean, preprocessed datasets (train/test)
• ✅ Comprehensive EDA with insights
• ✅ Deployment artifacts (scaler, encoder)
• ✅ Complete handoff documentation

👉 ML Team: Start with HANDOFF_TO_ML_TEAM.md