✈️ Aircraft Delay Log Intelligence

An end-to-end NLP pipeline for classifying operational aircraft delay logs. This project uses fine-tuned transformer models to predict the delay cause, operational phase, and predictability, enabling structured understanding of free-text reports from airline operations.

---

🔍 Example

Input:
Late pushback due to hydraulic pump issue discovered during final check.

Output:

• 🧠 Category: TECHNICAL_FAILURE
• 🧭 Phase: final check
• 🔮 Predictability: UNPREDICTABLE

---

🎯 Use Case

Airlines generate thousands of unstructured delay reports from pilots, crew, and ground staff. This tool transforms those into structured, actionable insights — useful for analytics, automation, and training.

---

🧠 Tasks

Task	Description
Text Classification	Predicts 6 delay types (e.g., TECHNICAL_FAILURE, WEATHER)
Phase Prediction	Predicts operational phase (e.g., boarding, pushback)
Predictability Estimation	Heuristic logic: certain delays are predictable
Interpretability	Attention-based token attribution
Streamlit App	Interactive interface for real-time predictions

---

🧾 Dataset

A synthetic dataset of 300+ logs was created using domain-aware GPT prompting, with the following fields:

• log_text (description)
• label (category)
• phase (operational step)
• severity (low, medium, high)

📁 File: data/synthetic_logs.csv

---

🏗️ Models

• bert-base-uncased fine-tuned using Hugging Face Transformers
• Separate models for:
  • Delay category: delay_classifier/
  • Operational phase: delay_phase_classifier/

---

🖥️ Streamlit Demo

Launch locally:

cd streamlit_app
streamlit run app.py

---

📊 Evaluation

📊 Evaluation Metrics

Task	Metric	Score
Delay Category Classification	Accuracy	1.00
	Precision (weighted)	1.00
	Recall (weighted)	1.00
	F1 Score (weighted)	1.00
Phase Prediction	Accuracy	1.00
	Precision (weighted)	1.00
	Recall (weighted)	1.00
	F1 Score (weighted)	1.00

🧩 Interpreting the Perfect Score

Altough the model achieves F1 = 1.00 on delay category and phase classification, this is expected due to the structured nature of operational delay logs. In real airline operations: Reports are written in concise, consistent templates Specific keywords (e.g., “crew rest”, “catering truck”) directly imply certain delay types GPT-generated synthetic data reflects this domain consistency Thus, the task is highly learnable — and the model's performance mirrors how easily human dispatchers or operations analysts could classify these entries

---

🧪 Sample Logs to Test

Flight held at gate due to ATC departure slot congestion.
→ Category: ATC_RESTRICTION | Phase: pre-departure | UNPREDICTABLE

Fueling truck arrived late, delaying pushback.
→ LOGISTICS_ISSUE | pre-departure | PREDICTABLE

Crew rest period exceeded; replacement crew dispatched.
→ CREW_DELAY | pre-departure | PREDICTABLE

---

📂 Project Structure

aircraft-delay-nlp/
├── data/                 # CSV + label maps
├── model/                # Saved models
├── notebooks/            # Training + inference
├── streamlit_app/        # Streamlit interface
├── requirements.txt
└── README.md

---

🔗 GitHub

github.com/Simo-dg/aircraft-delay-nlp